Aircraft Based GPS Augmentation Using an On-Board RADAR Altimeter for Precision

Home , Air navigation, Autoland, Autopilot, Avionics, GNSS Augmentation, Instrument approach

Approach and Landing of Unmanned Aircraft Systems

A thesis presented to

the faculty of

the Russ College of Engineering and Technology of Ohio University

In partial fulfillment

of the requirements for the degree

Master of Science

Andrew R. Videmsek

May 2020

This thesis titled

Aircraft Based GPS Augmentation Using an On-Board RADAR Altimeter for Precision

Approach and Landing of Unmanned Aircraft Systems

ANDREW R. VIDEMSEK

has been approved for

the School of Electrical Engineering and Computer Science

and the Russ College of Engineering and Technology by

Maarten Uijt de Haag

Adjunct Professor of Electrical Engineering and Computer Science

Mei Wei

Dean, Russ College of Engineering and Technology 3

ABSTRACT

VIDEMSEK, ANDREW R., M.S., May 2020, Electrical Engineering and Computer

Science

Aircraft Based GPS Augmentation Using an On-Board RADAR Altimeter for Precision

Approach and Landing of Unmanned Aircraft Systems

Director of Thesis: Maarten Uijt de Haag

With a growing demand for large unmanned aircraft system operations in the national airspace system, a method to safely and automatically land unmanned aircraft at a wide range of airports with varying levels of equipage is still needed. Currently no navigation system is capable of a fully coupled precision approach and landing without the use of ground based navigational aids. To enable widescale adoption and usage of unmanned aircraft systems, an aircraft based augmentation system that provides precision approach and landing service without sacrificing safety is required to land the aircraft at all runways.

This thesis proposes an aircraft based GPS augmentation system using an on-board downward facing radar altimeter for precision approach and landing of unmanned aircraft systems. The proposed architecture is initially evaluated using a simulation environment designed to test multiple different GNSS, radar altimeter, and terrain elevation database configurations. Following the offline simulation, a flight test analysis is completed testing the proposed architecture using pre-recorded flight test data at the Ohio University Airport

(OH) and Reno-Tahoe International Airport (NV). Furthermore, this thesis provides a sensitivity study on the systematic errors in the augmentation system to better characterize 4 and account for the inherent errors of the architecture’s subsystems. This thesis then discusses modifications to the previously developed terrain database spot algorithm to better account for the characteristics of the selected radar altimeter. Finally, an approach for future certification is proposed followed by recommendations for further research on the topic.

DEDICATION

To my family, friends, and colleagues

ACKNOWLEDGMENTS

I would like to give a special thanks to my advisor, Dr. Maarten Uijt de Haag, for his support, advice, and expertise on the research presented in this thesis. Without his encouragement and supervision this thesis would not have been possible.

Thank you to my committee members, Dr. Frank van Graas, Dr. Sabrina Ugazio, and Dr. Justin Frantz for their time and assistance in reviewing, critiquing, and improving my thesis.

I want to thank the members of General Atomics Aeronautical Systems Inc., including but not limited to Brandon Suarez, Timothy Bleakley, Jose Fuentes, Fabrice

Kunzi, and Xavier Redondo, for the assistance they have provided throughout every step of my masters. Additionally, I would like to thank General Atomics Aeronautical Systems

Inc. for providing the funding for this research.

I thank every member of the Ohio University Avionics Engineering Center. The professors, engineers, students, and staff of the center are some of the brightest people I have ever had the opportunity to work with. Each and every one of them has provided me with invaluable knowledge and guidance. I would not have been able to do it without them.

Most of all, I would like to thank my parents, Michael and Margaret Videmsek, for their support and encouragement. They have shaped me into the person I am today, always providing me with guidance in every goal I have pursued.

Thank you!

TABLE OF CONTENTS

Page

Abstract ...... 3 Dedication ...... 5 Acknowledgments ...... 6 Table of Contents ...... 7 List of Tables...... 10 List of Figures ...... 12 List of Acronyms and Abbreviations...... 14 1 Introduction ...... 17 1.1 Thesis Organization ...... 19 2 Unmanned Aerial Vehicle Background ...... 21 3 Navigation Performance Background ...... 24 3.1 Navigational Performance Parameters ...... 24 3.1.1 Accuracy ...... 24 3.1.2 Integrity ...... 24 3.1.3 Continuity ...... 27 3.2 Non-Approach Categories ...... 27 3.3 Approach Categories ...... 30 3.4 Navigational Requirements for RPA Autoland...... 34 4 Navigational Systems Background ...... 35 4.1 Instrument Landing System (ILS) ...... 35 4.2 Global Navigation Satellite Systems (GNSS) ...... 37 4.2.1 GPS Space Segment ...... 37 4.2.2 GPS Control Segment ...... 38 4.2.3 GPS User Segment ...... 39 4.2.4 GPS Error Sources ...... 42 4.3 Aircraft Based Augmentation Systems (ABAS) ...... 42 4.4 Satellite Based Augmentation System (SBAS) ...... 43 4.4.1 Localizer Performance with Vertical Guidance (LPV) with WAAS 44 4.5 Ground Based Augmentation System Landing System (GLS) ...... 45 8

4.6 Radar Altimeter ...... 47 5 Terrain Database Background ...... 51 5.1 World Geodetic System (WGS) 84 and Earth Gravitational Model (EGM) 96 51 5.2 Digital Elevation Models (DEM) ...... 52 5.3 Digital Terrain Elevation Data (DTED) ...... 54 5.4 Shuttle Radar Topography Mission (STRM) ...... 55 5.5 LiDAR Generated Terrain Elevation Databases ...... 57 5.6 Terrain Database Integrity ...... 57 5.7 Terrain Referenced Navigation (TRN) ...... 59 6 RADAR Altimeter Aiding (RALT Aiding) ...... 60 6.1 System Description and Concept of Operation ...... 62 6.2 RALT Aiding Method ...... 66 6.3 Position Computation ...... 66 6.3.1 Integrity Computation ...... 68 7 Feasibility Study ...... 72 7.1 Simulation Method ...... 72 7.1.1 Selected Error Statistics ...... 73 7.1.2 Accuracy Calculation Algorithm ...... 76 7.1.3 Protection Level Calculation Algorithm ...... 78 7.2 Feasibility Results ...... 78 8 Flight Test Analysis ...... 89 8.1 Description of Flight Tests...... 89 8.2 Analysis Method ...... 93 8.3 Flight Test Results ...... 94 8.3.1 KRNO Results ...... 94 8.3.2 KUNI Results ...... 99 9 Sensitivity and Error Analysis ...... 101 9.1 Systematic Errors ...... 102 9.1.1 Interpolation Error Analysis ...... 103 9.1.2 Lateral Offset Error Analysis ...... 109 9.1.3 Attitude Error Analysis ...... 117 9.1.4 Other Error Conditions ...... 120 9

9.1.4.1 Foliage ...... 120 9.1.4.2 Undetected Faults ...... 121 9.1.4.3 Age of Data ...... 121 9.2 Terrain Database Comparison ...... 121 10 Spot Algorithm Improvement Concept ...... 125 11 Approach for Certification ...... 131 11.1 FAA Standards Development Process ...... 131 11.2 Certification of RALT Aiding ...... 132 11.2.1 Standards Comparison ...... 134 12 Summary and Conclusions ...... 137 13 Recommendations for Future Work ...... 141 13.1 Additional Flight Tests ...... 141 13.2 Methods for Position Computation ...... 142 13.3 Modifications to RPA Approach ...... 143 References ...... 145 Appendix A : Single-Frequency GPS Error Statistics Computation ...... 152 Appendix B : Dual-Frequency GPS Error Statistics Computation ...... 153 Appendix C : WAAS Error Statistics Computation ...... 155 Appendix D : RAIM VPL And HPL Computation ...... 157 Appendix E : WAAS VPL And HPL Computation ...... 161

LIST OF TABLES

Page

Table 2-1: JUAS COE UAS Categories [8] ...... 22 Table 2-2: Informal UAS Classification [7] ...... 23 Table 3-1: Selected RNP Requirements [13] ...... 30 Table 3-2: Selected Non-Precision Approach Requirements ...... 33 Table 3-3: Selected Approach with Vertical Guidance Requirements ...... 33 Table 3-4: Selected Precision Approach Requirements ...... 34 Table 4-1: ILS Categories [10]...... 37 Table 4-2: GBAS Service Levels for Approach Service (derived from [10]) ...... 47 Table 5-1: DTED Specifications [26] ...... 55 Table 5-2: DTED1 Accuracy Specifications [26] ...... 55 Table 5-3: SRTM Specifications [27] ...... 56 Table 5-4: Localized SRTM Specifications [27] ...... 56 Table 6-1: GLS F Navigational Requirements...... 62 Table 7-1: Single Frequency SV Errors [2] ...... 74 Table 7-2: Dual Frequency GPS URA & URE ...... 74 Table 7-3: Selected WAAS Simulation Values ...... 75 Table 7-4: Simulated Terrain Database Error Statistics ...... 76 Table 7-5: Feasibility Study - Single-Frequency GPS Full Availability Results...... 84 Table 7-6: Feasibility Study - WAAS Full Availability Results ...... 84 Table 7-7: Feasibility Study - Dual-Frequency GPS Case 1 Full Availability Results ..... 85 Table 7-8: Feasibility Study - Dual-Frequency GPS Case 2 Full Availability Results ..... 85 Table 7-9: Feasibility Study - Dual-Frequency GPS Case 3 Full Availability Results ..... 86 Table 7-10: Feasibility Study - Dual-Frequency GPS Case 4 Full Availability Results ... 86 Table 8-1: Flight Test Results - KRNO Approach #1 ...... 94 Table 8-2: Flight Test Results - KRNO Approach #2 ...... 97 Table 8-3: Flight Test Results - KRNO Approach #3 ...... 98 Table 8-4: Flight Test Results - KRNO Approach #4 ...... 98 Table 8-5: Flight Test Results - KRNO Approach #5 ...... 98 Table 8-6: Flight Test Results - KUNI Approach ...... 99 Table 9-1: KUNI Interpolation Error Results ...... 108 11

Table 9-2: KRNO Interpolation Error Results ...... 108 Table 9-3: Lateral Offset Induced Vertical Error - KUNI Approach ...... 112 Table 9-4: Lateral Offset Induced Vertical Error - KRNO Approach ...... 113 Table 9-5: Lateral Offset Error Induced Vertical Error - KUNI Approach Results with Reduced Lateral Error...... 115 Table 9-6: Lateral Offset Error Induced Vertical Error - KRNO Approach Results with Reduced Lateral Error...... 116 Table 9-7: Difference between Terrain Databases and Synthesized Terrain ...... 123 Table 10-1: Difference between KUNI DTED1 Spot Algorithm Terrain and Synthesized Terrain ...... 128 Table 10-2: Difference between KUNI LiDAR Spot Algorithm Terrain and Synthesized Terrain ...... 128 Table 10-3: Difference between KRNO DTED1 Spot Algorithm Terrain and Synthesized Terrain ...... 129 Table 10-4: Difference between KRNO LiDAR Spot Algorithm Terrain and Synthesized Terrain ...... 130

Table A-1: Single Frequency SV Errors [2] ...... 152 Table D-1: K-Multipliers for Faulted Protection Level Calculations ...... 159

LIST OF FIGURES

Page

Figure 3-1: Protection Level Volume ...... 27 Figure 3-2: RNAV/RNP Accuracy and Containment Limit ...... 29 Figure 3-3: RNP Total System Error ...... 29 Figure 3-4: Approach Type Performance (derived from [15]) ...... 32 Figure 4-1: ILS Localizer and Glideslope Radiation Patterns [14] ...... 36 Figure 4-2: Typical GBAS Architecture [22] ...... 46 Figure 4-3: Radar Altimeter Beamwidth ...... 49 Figure 4-4: Radar Altimeter Illumination Zone ...... 50 Figure 5-1: Geoid Undulation ...... 52 Figure 5-2: Terrain Database Interpolation...... 53 Figure 6-1: RALT Aiding Architecture ...... 61 Figure 6-2: Radar Altimeter Derived Range Height Corrections ...... 64 Figure 6-3: RALT Aiding Concept Operational Range ...... 66 Figure 7-1: HPL & VPL for Dual Frequency Case #3 Without RALT Aiding (result ID 61) ...... 79 Figure 7-2: HPL & VPL for Dual Frequency Case #3 Without RALT Aiding (σ = 1.0m) (result ID 70) ...... 81 Figure 7-3: 2D Histogram of Vertical Error and VPL for Dual Frequency Case #3 with RALT Aiding (σ = 1.0m) (result ID 70) ...... 82 Figure 7-4: 2D Histogram of Horizontal Error and HPL for Dual Frequency Case #3 with RALT Aiding (σ = 1.0m) (result ID 70) ...... 83 Figure 8-1: Flight Path of KRNO Approach #1 - Satellite Overlay ...... 90 Figure 8-2: Flight Path of KRNO Approach #1 - Elevation Overlay (DTED1) ...... 90 Figure 8-3: Flight Path of KUNI Approach - Satellite Overlay ...... 91 Figure 8-4: Figure 9: Flight Path of KUNI Approach - Elevation Overlay (DTED1) ...... 92 Figure 8-5: Flight Path of KUNI Approach - Elevation Overlay (LiDAR)...... 92 Figure 8-6: 2D Histogram of Horizontal Performance for KRNO Approach #1 - GPS Only ...... 95 Figure 8-7: 2D Histogram of Vertical Performance for KRNO Approach #1 - GPS Only ...... 96 Figure 8-8: 2D Histogram of Horizontal Performance for KRNO Approach #1 - RALT Aiding ...... 96 13

Figure 8-9: 2D Histogram of Vertical Performance for KRNO Approach #1 - RALT Aiding ...... 97 Figure 9-1: RALT Aiding Architecture with Off Nominal Conditions...... 101 Figure 9-2: Interpolation Error ...... 104 Figure 9-3: KUNI Approach Test Area – Satellite Overlay ...... 105 Figure 9-4: KUNI Approach Test Area – LiDAR Elevation Overlay...... 106 Figure 9-5: KRNO Approach Test Area – Satellite Overlay ...... 106 Figure 9-6: KRNO Approach Test Area – LiDAR Elevation Overlay ...... 107 Figure 9-7: Lateral Offset Induced Error ...... 110 Figure 9-8: Lateral Offset Test Zone Spacing...... 111 Figure 9-9: Lateral Error Induced Vertical Error - KUNI Approach ...... 112 Figure 9-10: Lateral Offset Induced Vertical Error - KRNO Approach ...... 113 Figure 9-11: Lateral Offset Error Induced Vertical Error - KUNI Approach with Reduced Lateral Error ...... 115 Figure 9-12: Lateral Offset Error Induced Vertical Error - KRNO Approach with Reduced Lateral Error...... 116 Figure 9-13: Pitch Induced Height Error between GPS and Radar Altimeter Antennas 117 Figure 9-14: Pitch Induced Radar Altimeter Measurement Error ...... 119 Figure 9-15: Radar Altimeter Antenna Beamwidth Scan Area ...... 120 Figure 9-16: Terrain Height below Aircraft during KUNI Approach ...... 122 Figure 9-17: Difference between Terrain Databases and Synthesized Terrain ...... 123 Figure 10-1: Spot Algorithm Concept of Operation...... 126 Figure 10-2: KUNI Spot Algorithm Elevation Differences ...... 127 Figure 10-3: KRNO Spot Algorithm Elevation Differences ...... 129 Figure 11-1: Avionics Standards Development Process (derived from [41]) ...... 132 Figure 11-2: RALT Aiding Airborne Equipment ...... 133 Figure 13-1: CAT III Autoland Touchdown Performance Requirements (Derived from [10]) ...... 143 Figure 13-2: Modified RPA Approach and Landing ...... 144

LIST OF ACRONYMS AND ABBREVIATIONS

ABAS Aircraft Based Augmentation System AFB Air Force Base AGL Above Ground Level AL Alert Limit APV Approach with Vertical Guidance ARAIM Advanced RAIM ARINC Aeronautical Radio, Inc. ARNS Aeronautical Radionavigation Service CAT Category CGS Canadian Geodetic Survey CSRS Canadian Spatial Reference System DA Decision Altitude DEM Digital Elevation Model DH Decision Height DLIM Downward Looking Terrain Database Monitor DME Distance Measuring Equipment DoD Department of Defense DTED Digital Terrain Elevation Data EASA European Aviation Safety Agency EGM Earth Gravitational Model EGNOS European Geostationary Navigation Overlay Service ESA European Space Agency EWR Newark Liberty International Airport FAA Federal Aviation Administration FM-CW Frequency-Modulated Continues-Wave FTE Flight Technical Error GBAS Ground Based Augmentation System GCS Ground Control Station GEO Geosynchronous Equatorial Orbit GLS GBAS Landing System GNSS Global Navigation Satellite Systems GPS Global Positioning System 15

GPWS Ground Proximity Warning Systems GSL GBAS Service Level HAL Horizontal Alert Limit HMI Hazardously Misleading Information HPL Horizontal Protection Level IAH George Bush Intercontinental Airport ICAO International Civil Aviation Organization ILS Instrument Landing System IMC Instrument Meteorological Conditions INS Inertial Navigation System JUASCOE Joint Unmanned Aircraft Systems Center of Excellence KRNO Reno-Tahoe International Airport KUNI Ohio University Airport LiDAR Light Detection and Ranging LLS Linearized Least Squares LNAV/VNAV Lateral Navigation/Vertical Navigation LPV Localizer Performance with Vertical Guidance MALE Medium Altitude-Long Endurance MDA Minimum Descent Altitude MDH Minimum Descent Height MEO Medium Earth Orbit MI Misleading Information MSL Mean Sea Level NAS National Airspace System NASA National Aeronautics and Space Administration Navaids Navigation Aid NDB Nondirectional Beacon NGA National Geospatial-Intelligence Agency NPA Non-Precision Approach NSE Navigation System Error PA Precision Approach PBN Performance Based Navigation PDE Path Definition Error PL Protection Level 16

PNT Position, Navigation, and Timing POFZ Precision Obstacle Free Zone PPS Precise Point Positioning PRF Pulse Repetition Frequency RAIM Receiver Autonomous Integrity Monitoring RALT Radar Altimeter RNAV Area Navigation RNP Required Navigation Performance ROFA Runway Object Free Area ROFZ Runway Obstacle Free Zone RPA Remotely Piloted Aircraft RPZ Runway Protection Zone RSA Runway Safety Area SBAS Satellite Based Augmentation Systems SRTM Shuttle Radar Topography Mission STS Space Transportation System sUAS Small Unmanned Aerial Systems SV Space Vehicle TRN Terrain Referenced Navigation TSE Total System Error TTA Time to Alert UAV Unmanned Aerial Vehicle UEE User Equipment Error URA User Range Accuracy URE User Range Error USAF United States Air Force VDB VHF Data Broadcast VHF Very High Frequency VOR VHF Omnidirectional Range VPL Vertical Protection Level WAAS Wide Area Augmentation System WGS World Geodetic System WLS Weighted Least Squares

1 INTRODUCTION

Aviation is highly dependent on systems that provide position, navigation, and timing (PNT) information to the pilot or aircraft to enable safe navigation through the

National Airspace System (NAS). Throughout aviation history, the United States and

Europe have relied on several navigation systems to provide this information during en- route flight including nondirectional beacons (NDB), Very High Frequency (VHF)

Omnidirectional Range (VOR), distance measuring equipment (DME), and Loran/Loran-

C. While many of these systems are still in use to some capacity today, en-route navigation has become heavily reliant on Global Navigation Satellite Systems (GNSS), mainly the

Global Positioning System (GPS), and onboard systems such as inertial navigations systems (INS). The rapid development and wide-scale adoption of GPS in aviation navigation has allowed for more advanced and efficient aircraft routing and has enabled additional non-precision approach (NPA) options such as Lateral Navigation/Vertical

Navigation (LNAV/VNAV) and approach with vertical guidance (APV) systems such as

Localizer Performance with Vertical Guidance (LPV). While GPS and GPS-based systems have become the de-facto standard for non-precision approach and approach with vertical guidance systems, precision approach and landing systems, the highest of which allow for aircraft autoland, still require ground based navigational infrastructure at or near the runway. These precision approach systems, such as the Instrument Landing System (ILS) and the Ground Based Augmentation System (GBAS) Landing System (GLS), all require expensive installations on the airport grounds. The cost of these systems is easily justified at airports with large volumes of passenger and/or cargo traffic due to the associated gain 18 in safety and accessibility during instrument meteorological conditions (IMC), noise abatement measures, and new approach procedures. At smaller civil airports, the installation of the above systems has been cost-prohibitive and therefore aircraft are unable to fly a precision approach in many of these airports. Thus, the majority of aircraft landing at smaller airports are not equipped for, nor are the pilots trained for, the ground based navigational systems used in a precision approach and landing.

Recently, there has been increasing interest to integrate large unmanned aerial vehicles (UAVs) [1], henceforth called Remotely Piloted Aircraft1 (RPA), into the NAS.

Given the nature of the RPA operations, they are not only expected to operate out of airports with precision approach landing systems, but also out of smaller airports with non- precision approach procedures only.

With no pilot on board the aircraft, there are three methods to land an RPA. Firstly, the RPA can be flown visually by a remote pilot using a video feed from an on-board camera via a low-latency datalink between the RPA and the ground control station (GCS).

This method is currently used for large military RPAs and requires skilled pilots and a GCS located at the operating airfield. Secondly, a ground navigation aid (navaids) can be installed at all airports that expect RPA traffic and all RPAs must be equipped and certified to use this navaid. The infrastructure costs associated with this approach would likely limit

1 ICAO defines Remotely Piloted Aircraft as “an unmanned aircraft which is piloted from a remote pilot station” [58], in some cases this definition can be too broad and include smaller, heavier than air aircraft that do not require the same performance standards as a manned airplane. The term RPA used in this study refers unmanned airplanes designed to fly with similar regulations to manned aircraft, take-off and land in a conventional manner, and operate in the same airspace as manned aircraft. This definition currently includes aircraft such as the General Atomics MQ-1 Predator, General Atomics MQ-9 Reaper, and Northrop Grumman RQ-4 Global Hawk, but is not limited to these aircraft as further RPA development is expected. 19 the spread of RPA use. Finally, an on-board navigation system could be developed and that does not require any ground-based infrastructure at the airport and has sufficient accuracy, integrity, and continuity to be certified for automatic landing of RPAs.

While no system currently exists for precision approach and landing without ground based navaids, an on-board navigation system would allow for the greatest operational freedom and enable RPA traffic at the largest number of airports. Additionally, a system such as this could be adapted and used for precision approach and landing of general aviation aircraft. The feasibility analysis and development of such a system is the focus of this thesis.

1.1 Thesis Organization

Chapter 2 provides a short introduction to UAVs and RPAs. Chapter 3 introduces aircraft navigation performance parameters and approach and non-approach categories.

Chapter 4 provides a brief background on navigation systems including an overview of

GPS, the Wide Area Augmentation Systems (WAAS), ILS, GLS, and radar altimeters.

Chapter 5 discusses terrain elevation databases or digital elevation models (DEM) and terrain referenced navigation (TRN). Chapter 6 provides an in-depth discussion of the

RALT Aiding architecture. The discussion includes the approach to integrate the radar altimeter and GNSS measurements as well as a terrain elevation database to provide a position solution for use in navigation. In Chapter 7, simulations are used to analyze the feasibility of RALT Aiding, and determine the expected real-world performance, the shortcomings, and limitations of the system. This analysis includes simulation cases for

RALT Aiding when using single-frequency GPS, dual-frequency GPS, and GPS 20 augmented with WAAS, in addition to five different terrain elevation database accuracies.

Building on Chapter 7, Chapter 8 presents a similar study but determines the performance of the RALT Aiding system in a real-world situation by using GPS and radar altimeter data recorded on two previous flight tests. Chapter 9 discusses a sensitivity analysis of RALT

Aiding and examines the systematic error conditions that could impact the performance of the system. This sensitivity analysis studies the sensitivity to DEM interpolation errors, lateral position errors, and errors due to aircraft attitude. Chapter 10 discusses the spot algorithm used to improve the information in the DEM through different DEM polling methods. Chapter 11 provides a short discussion on the expected path to certification for a system such as this. Chapters 12 and 13 summarize, draw conclusions, and provides suggestions for future work on RALT Aiding.

A large portion of this thesis is based on previous research published by the author in [2], [3], and [4].

2 UNMANNED AERIAL VEHICLE BACKGROUND

In recent years there has been a large growth in the use and availability of unmanned aerial vehicles. The majority of this growth was originally started at the hobbyist level by low cost, high volume small unmanned aerial systems (sUAS), often referred to as drones, such as the Parrot AR. Drone and DJI Phantom [5]. Initially designed for amateurs and hobbyists, these vehicles quickly found commercial and research applications, expediting their growth. At the time of writing, it is estimated that there are over 1.3 million registered sUAS in the United States [6].

While sUAS have seen a recent explosion in popularity due to low cost communications, navigation, and flight controllers, UAS are not a new concept. Until recently, UAV development was mostly for military applications. Most consider the first true UAV to be either the Kettering Bug, first flying in 1918 with preset controls [7], or the DH.82 Queen Bee targeting drone whose first radio-controlled flight was in 1935 [1].

Following the lead of the Queen Bee, RPAs, as they are commonly referred to as, have seen widescale military use since the cold war [7]. Today, many militaries use a wide range of UAVs, from small multirotor UAVs to fixed wing RPAs with a wingspan of 130 feet, such as the Northrop Grumman RQ-4 Global Hawk, which has a longer wingspan than a

Boeing 737. While many of the smaller military UAV are flown at or near the battle field through hand held remote controls, the larger UAVs, such as the General Atomics MQ-9

Reaper are flown by highly trained pilots in a GCS hundreds or thousands of miles away from the aircraft. The use of highly trained pilots is due to the higher cost and higher risk nature of the larger RPAs [5]. 22

Currently, there is no single standard classification system for UAVs, but multiple competing systems each used within a respective group. A common system used in military application is the US Department of Defense (DoD) Joint Unmanned Aircraft Systems

Center of Excellence (JUAS COE) five group system. This system classifies UAS by their maximum gross takeoff weight, normal operating altitude, and speed. This classification systems in presented in Table 2-1.

Table 2-1: JUAS COE UAS Categories [8] Maximum Normal UAS Representative Gross Takeoff Operating Speed (KIAS) Category UAS Weight (Ibs) Altitude (ft) Group 1 0-20 < 1,200 AGL 100 kts WASP III Group 2 21-55 < 3,500 AGL < 250 kts ScanEagle Group 3 < 1,320 < 18,000 MSL < 250 kts RQ-7B Shadow MQ-1A/B/C Group 4 > 1,320 < 18,000 MSL Any Airspeed Predator MQ-9A Reaper Group 5 > 1,320 > 18,000 MSL Any Airspeed & RQ-4 Global Hawk

Outside of military applications, a more informal classification system is used based on the size of the UAV. Reference [7] proposes a simple system based on size that is convenient for discussion purposes, but not extremely rigorous on classification requirements. This system is listed in Table 2-2.

Table 2-2: Informal UAS Classification [7] UAS Category UAS Size Representative UAS IAI Malat Mosquito & Dimensions on the order of Very Small UAV Aurora Flight Sciences 30-50 cm Skate At least one dimension RQ-11 Raven & Small UAV greater than 50 cm CyberQuad Mini Wingspans of the order of RQ-2 Pioneer & RQ-5 Medium UAV 5–10 m Hunter Larger MQ-9A Reaper & RQ-4 Large UAV than a typical light manned Global Hawk aircraft

3 NAVIGATION PERFORMANCE BACKGROUND

3.1 Navigational Performance Parameters

When discussing the navigational requirements for precision approach (PA), NPA, and APV, the requirements are broken down into three categories: accuracy, integrity, and continuity. In simple terms, accuracy is how accurate the estimated position is to the true position, integrity is the value of trust that can be applied to that position estimate, and continuity is the availability of that system during the given operation.

3.1.1 Accuracy

Navigational accuracy is defined as “the accuracy of an estimated or measured position of a craft at a given time is the degree of conformance of that position with the true position of the craft at that time. Since accuracy is a statistical measure of performance, a statement of navigation system accuracy is meaningless unless it includes a statement of the uncertainty in position that applies” [9]. In most aviation navigation cases, the statement of uncertainty is specified as a 95% value, or 2 sigma, in one dimensional terms [9]. For

PA, NPA, and APV approaches these accuracy values are stated in terms of lateral (or horizontal) and vertical accuracies. ILS and GLS define lateral error as the position error perpendicular to the direction of the runway whereas WAAS (LPV-200 & LPV) define its lateral error as a horizontal error, the norm of the east and north error.

3.1.2 Integrity

Navigational integrity is defined as “the measure of the trust that can be placed in the correctness of the information supplied by a navigation system. Integrity includes the ability of the system to provide timely warnings to users when the system should not be 25 used for navigation” [9]. The integrity requirements are broken down into four different parameters: (i) integrity probability, (ii) time to alert, (iii) alert limits, (iv) and protection levels.

i. Integrity probability: The probability that the position error does not exceed the

value of the set alert limit. It can also be denoted as the probability of hazardous

misleading information (푃퐻푀퐼) where integrity probability is calculated as:

푃퐼푛푡푒푔푟푖푡푦 = 1 − 푃퐻푀퐼 (1)

Where 푃퐻푀퐼 is calculated as the probability that the position error is larger than the

alert limit and an integrity failure:

푃퐻푀퐼 = 푃푟(|훿풙̂| > 퐴퐿 ∩ 푑 < 푇) (2)

Where misleading information (MI) is defined as “a navigation system error that

exceeds the alert limit without annunciation” [10].

ii. Time to alert (TTA): “The maximum allowable time interval between system

performance ceasing to meet operational performance limits and the appropriate

integrity monitoring subsystem providing an alert” [10].

ii. Alert limits (AL): “For a given parameter measurement, the error tolerance not

to be exceeded without issuing an alert” [10]. Where alert is defined as “an

indication provided to other aircraft systems or annunciation to the pilot to identify

that an operating parameter of a navigation system is out of tolerance” [10]. 26

a. Horizontal alert limit: The “radius of a circle in the horizontal plane, (the

local plane to the WGS-84 ellipsoid), with its center being at the true

position, that describes the region that is required to contain the indicated

horizontal position with the required probability for a particular navigation

mode” [11].

b. Vertical alert limit: The “half length of a segment on a vertical axis

(perpendicular to the horizontal plane of the WGS-84 ellipsoid), with its

center being at the true position, that describes the region that is required to

contain the indicated vertical position with… [a given probability]… for a

particular navigation mode” [11]. iv. Protection level (PL): The “statistical error value that bounds the actual error

(NSE [Navigational System Error] in particular) with a specified confidence” [10].

The protection level volume is illustrated in Figure 3-1.

a. Horizontal protection level (HPL): The “radius of a circle in the

horizontal plane (the plane tangent to the WGS-84 ellipsoid), with its center

being at the true position, that describes the region assured to contain the

indicated horizontal position” [11].

b. Vertical protection level (VPL): The “half the length of a segment on the

vertical axis (perpendicular to the horizontal plane of the WGS-84

ellipsoid), with its center being at the true position, that describes the region

assured to contain the indicated vertical position” [11].

HPL

VPL

Figure 3-1: Protection Level Volume

3.1.3 Continuity

Continuity is defined as the “ability of the total system to perform its function without the interruption during the intended operation. More specifically, continuity is the probability that the specified systems performance will be maintained for the duration of a phase of operation, presuming that the system was available at the beginning of that phase of operation and was predicted to operate throughout the operation” [11].

3.2 Non-Approach Categories

Due to the ever growing demand for the efficient use of airspace, the International

Civil Aviation Organization (ICAO), Federal Aviation Administration (FAA), European 28

Aviation Safety Agency (EASA), and other state aviation bodies, with the help of technical guidance organizations such as RTCA, have begun developing and implementing standards and regulations to replace conventional ground based flight routes with performance based routes. This concept, called performance-based navigation (PBN), bases navigational requirements on operational requirements to replace sensor specific routes with performance requirements routes. This change allows aircraft that meet a certain navigational performance level to fly a certain route, without requiring it to have a specific sensor suite [12].

PBN builds on the previously established area navigation (RNAV) and required navigational performance (RNP) standards to create a more complete requirement. By combining RNAV and RNP into one system, the accuracy requirements from RNAV are used with the requirement for on-board performance monitoring coming from the RNP specification. RNAV/RNP values are given in terms of nautical mile (nmi) lateral accuracy limits in each cross-track direction from the desired flight path. The containment limit is then defined as two times the cross-track accuracy limit [12]. These relationships are illustrated in Figure 3-2.

Containment Limit ( . )

Accuracy Limit ( ) 2 R P Value

Desired Flight Path

R P Value Accuracy Limit ( )

Containment Limit ( . )

Figure 3-2: RNAV/RNP Accuracy and Containment Limit

The accuracy of the aircraft during an RNP procedure is known as the total system error (TSE), and is RSS of the path definition error (PDE), flight technical error (FTE), and navigation systems error (NSE) [12]. These errors are explained in Figure 3-3.

Desired Flight Path Path Definition rror Defined Flight Path Total ystem Flight Technical rror rror stimated Position avigation ystem rror True Position

Figure 3-3: RNP Total System Error

Each RNP level has an associated 95% horizontal accuracy, integrity probability, horizontal alert limit (HAL), and continuity probability. These requirements are listed in

Table 3-1.

Table 3-1: Selected RNP Requirements [13] Accuracy Integrity Continuity Horizontal Integrity Horizontal Alert Continuity Procedure Accuracy 95%2 3 Probability Limit Probability RNP 0.3 0.3 nmi (55 6m) 1-10-5 per hour 0.6 nmi 1-10-4 per hour (Approach) RNP 1 1 nmi 1-10-5 per hour 2 nmi 1-10-4 per hour (Terminal) RNP 2 2 nmi 1-10-5 per hour 4 nmi 1-10-4 per hour (En-route) RNP 4 4 nmi 1-10-5 per hour 8 nmi 1-10-4 per hour (Oceanic)

3.3 Approach Categories

Currently, multiple systems exist to perform precision and non-precision approaches into civilian airports. These systems are placed in one of three categories, PA,

NPA, and APV. According to the FAA, a precision approach is “a standard instrument approach procedure in which an electronic glideslope/or other type of glidepath is provided; e.g., ILS, PAR [Precision Approach Radar], and GLS” [14]. A non-precision approach is defined as a “standard instrument approach procedure in which no electronic glideslope is provided; e.g., VOR, TACAN [Tactical Air Navigation System], NDB, LOC

[Localizer], ASR [Airport Surveillance Radar], LDA [Localizer Type Directional Aid], or

2 ILS and GLS define lateral error as the position error perpendicular to the direction of the runway 3 WAAS (LPV-200, LPV, & LNAV/VNAV) define lateral error as a horizontal error, the norm of the east and north error 31

SDF [Simplified Directional Facility] approaches” [14]. Finally, an APV is defined as “an instrument approach based on a navigation system that is not required to meet the precision approach standards of ICAO Annex 10 but provides course and glidepath deviation information. For example, Baro−VNAV, LDA with glidepath, LNAV/VNAV and

LPV” approaches [14]. The main operational difference between the different approach classifications is the Decision Altitude (DA)/Decision Height (DH)4 or Minimum Descent

Altitude (MDA)/Minimum Descent Height (MDH)5. This value determines the height above the touchdown point at which the pilot must decide to continue the approach if correct visual reference has been established or to initiate a missed approach procedure.

The lower the DA/DH, the more stringent the navigational requirements are for the corresponding system. If the approach has a zero-foot decision height, no visual reference needs to be established during the approach and the aircraft can land and rollout without any decision from the pilot. In Figure 3-4 the DH and MDH for common approaches are presented with the corresponding distance from the touchdown point at which the decision must be made.

4 “A specified altitude or height (A/H) in the precision approach at which a missed approach must be initiated if the required visual reference to continue the approach has not been established” [14] 5 “The lowest altitude, expressed in feet above mean sea level, to which descent is authorized on final approach or during circle-to-land maneuvering in execution of a standard instrument approach procedure where no electronic glideslope is provided” [14] 32

Figure 3-4: Approach Type Performance (derived from [15])

Table 3-2, Table 3-3, and Table 3-4 list common NPA, APV, and PA approach categories and their associated accuracy, integrity, and continuity. The majority of the listed approaches are accomplished using navigations systems such as Satellite Based

Augmentation Systems (SBAS), GLS, and ILS. These systems are further explored in chapter 4.

Table 3-2: Selected Non-Precision Approach Requirements Accuracy Integrity Continuity Lateral Vertical Time Vertical NSE NSE Integrity Horizontal Continuity Approach to Alert Accuracy Accuracy Probability Alert Limit Probability alert Limit 95% 6 95% 1-8x10-6 in LP 1-10-7 per 16.0 m - 6.2 s 40.0 m - any 15 [11] approach seconds LNAV 1-10-7 per 1-5x10-5 per 220.0 m - 10 s 556.0 m - [16] hour hour

Table 3-3: Selected Approach with Vertical Guidance Requirements Accuracy Integrity Continuity Lateral Vertical Time Vertical NSE NSE Integrity Horizontal Continuity Approach to Alert Accuracy Accuracy Probability Alert Limit Probability alert Limit 95%7 8 95% 1-8x10-6 in LPV-200 1-10-7 per 16.0 m 4.0 m 6.2 s 40.0 m 35.0 m any 15 [11] [16] approach seconds GSL B 1-2x10-7 in 1-8x10-6 in 16.0 m 8.0 m 6 s 40.0 m 20.0 m [10] any 150 sec any 15 sec 1-8x10-6 in LPV 1-10-7 per 16.0 m 20.0 m 6.2 s 40.0 m 50.0 m any 15 [11] [16] approach seconds GSL A 1-2x10-7 in 1-8x10-6 in 16.0 m 20.0 m 10 s 40.0 m 50.0 m [10] any 150 sec any 15 sec LNAV/ 1-5.5x10-5 1-2x10-7 per VNAV 220.0 m 20.0 m 10 s 556.0 m 50.0 m in any 15 approach [16] sec

6 WAAS (LP & LNAV) define lateral error as a horizontal error, the norm of the east and north error 7 ILS and GLS define lateral error as the position error perpendicular to the direction of the runway 8 WAAS (LPV-200, LPV, & LNAV/VNAV) define lateral error as a horizontal error, the norm of the east and north error 34

Table 3-4: Selected Precision Approach Requirements Accuracy Integrity Continuity Lateral Vertical Time Horizontal Vertical NSE NSE Integrity Continuity Approach to Alert Alert Accuracy Accuracy Probability Probability alert Limit10 Limit 95%9 95% 1-2x10-6 in 1-1x10-9 in any 15 sec GSL F any 15 sec vertical, 1- 5.0 m 2.9 m 2 s 17.0 m 10.0 m [10] vertical, 30 2x10-6 for sec lateral any 30 sec lateral 1-1x10-9 in GSL E any 15 sec 1-4x10-6 in 5.0 m 2.9 m 2 s 17.0 m 10.0 m [10] vertical, 30 any 15 sec sec lateral 1-1x10-9 in GSL D any 15 sec 1-8x10-6 in 5.0 m 2.9 m 2 s 17.0 m 10.0 m [10] vertical, 30 any 15 sec sec lateral 1-0.5x10-9 1-2x10-6 vertical and vertical and CAT IIIb 5.0 m 3.0 m 1-0.5x10-9 2 s 17.0 m 10.0 m 1-2x10-6 [10] lateral in any lateral for one landing any 15 sec 1-0.5x10-9 1-2x10-6 vertical and vertical and CAT II 5.0 m 3.0 m 1-0.5x10-9 2 s 17.0 m 10.0 m 1-2x10-6 [10] lateral in any lateral for one landing any 15 sec GSL C 1-2x10-7 in 1-8x10-6 in 16.0 m 4.0 m 6 s 40.0 m 10.0 m [10] any 150 sec any 15 sec 1-10-7 1-4x10-6 vertical and vertical and CAT I 16.0 m 4.0 m 1-10-7 lateral 6s 40.0 m 10.0 m 1-4x10-6 [10] in any one lateral for landing any 15 sec

3.4 Navigational Requirements for RPA Autoland

The architecture proposed in this thesis and in [2] is targeted to meet the navigation requirements for a full autoland procedure with a zero-foot decision height and a zero-foot

9 ILS and GLS define lateral error as the position error perpendicular to the direction of the runway 10 HAL and VAL values for ILS are derived in [10] and are not found in the ILS specification 35 runway visual range. This approach performance is similar to that of an ILS CAT IIIb, GSL

D, or GSL F precision approach and landing. For the purpose of developing and testing the architecture, this thesis compares its results to the performance requirements of GSL D and

F. An ILS CAT IIIb, GSL D, and GSL F approach all have no DH or a DH of less than

50ft and an RVR of no less than 50 meters. With an RPA, the RVR may not be relevant to the landing operation if the rollout is fully automated. An RVR limit may still be imposed if the taxiing operation is to be conducted visually by a remote operator.

4 NAVIGATIONAL SYSTEMS BACKGROUND

The architecture proposed in this thesis builds on well-established navigation systems and technologies. This chapter serves as a simple background and review of all relevant RALT Aiding navigation systems.

4.1 Instrument Landing System (ILS)

ILS is a ground-based radio navigation system that allows an aircraft to calculate its lateral and vertical deviation from the set approach path. ILS is classified as a precision approach system and can support DA/DHs as low as zero-foot. ILS operates through two sets of antenna arrays, one located at the departure end of the runway and one located to the side of the runway at the runway touchdown point. The localizer antenna array, located at the end of the runway, provides the lateral guidance to the aircraft while the glideslope antenna array to the side of the runway provides vertical guidance. Each antenna array radiates two sideband signals, one at 90Hz and one at 150Hz, one on each side of the centerline. By measuring and comparing the depth of modulation of the radiated sidebands, 36 the aircraft is able to determine its angular deviation from the centerline and correct its flight accordingly. Figure 4-1 illustrates the ILS localizer and glideslope radiation patterns.

Figure 4-1: ILS Localizer and Glideslope Radiation Patterns [14]

ILS performance is divided into 3 different categories, some of which are further broken down into sub-categories. The different categories determine the DA/DH and the minimum runway visual range (RVR)11 of the approach. Table 4-1 lists the different FAA

ILS categories and their associated DH and RVR.

11 The horizontal distance a pilot will see down the runway from the approach end [14] 37

Table 4-1: ILS Categories [10] Category Decision Height RVR Category I (CAT I) >200ft (60m) >1,800ft (550m) Category II (CAT II) 100-200ft (30-60m) >1,200ft (350m) Category III (CAT III) <100ft (30m) or no DH <1,200ft (350m) Category IIIa (CAT IIIa) <100ft (30m) or no DH >700ft (200m) Category IIIb (CAT IIIb) <50ft (15m) or no DH 150-700ft (50-200m) Category IIIc (CAT IIIc) No DH <150ft (50m)

4.2 Global Navigation Satellite Systems (GNSS)

Global Navigation Satellite System is a generic term for a constellation of satellites in orbit that broadcast ranging signals down towards earth to be used for PNT purposes.

Currently there are two fully operational GNSS systems, GPS owned and operated by the

United States Air Force (USAF) and GLONASS operated by the Russian Federation.

Additionally, there are currently two GNSS systems under development, Galileo operated by the European Union, which is expected to be fully operational by 2020, and BeiDou operated by the People's Republic of China, also expected to be fully operational by 2020.

In terms of aviation, GPS is currently the only widely used GNSS system for navigation.

The GPS architecture consists of three major segments: space segment, control segment, and user segment [17].

4.2.1 GPS Space Segment

The GPS Space Segment is currently comprised of 31 operational space vehicles

(SV) in medium earth orbit (MEO) at an altitude of 20,200 km (~12500 miles). The SVs operate in 6 orbital planes, each with an inclination angle of 55 degrees and have an orbital period of approximately one-half sidereal day (11 hours, 58 minutes, 2 seconds). In addition to the 31 active satellites, there are currently 9 SVs on in-orbit reserve. When 38 initially developed, GPS was designed to operate with 24 satellites, allowing for 5 to be in view of a user globally, with a minimum of 4 being needed to compute the users position and time [17].

All GPS SVs broadcast a minimum of two ranging signals, L1 (1575.42 MHz) and

L2 (1227.6 MHz). L1 carries a course/acquisition (C/A) code and a precise encrypted

(P(Y)) code for use in military applications. The L2 signal only carries the P(Y) code. In addition to the ranging code, a navigation message is also modulated onto the C/A and

P(Y) codes to provide date, time, satellite status, almanac12, and ephemeris13 information

[17].

As new generations of GPS SVs are developed and launched, new ranging signals are added to the SV broadcasts in addition to the L1 and L2 signals. These signals include, at varying levels of deployment throughout the constellation, L1C (L1 Civilian), L1M (L1

M-code), L2C (L2 Civilian), L2M (L2 M-code), and L5 (1176.45 MHz). As with L1, L5 transmits inside a protected aeronautical radio navigation frequency band and can be used for aviation navigation, all other ranging signals cannot be used for aviation as they are not in protected frequency bands [17].

4.2.2 GPS Control Segment

The GPS Control Segment consists of three major parts: monitoring stations, the master control station, and ground antennas. At the time of writing, there are 16 GPS monitoring stations distributed across the globe, six owned and operated by the USAF and

12 Low resolution orbital information on all satellites 13 High resolution orbital information on the transmitting satellite 39

10 operated by the National Geospatial-Intelligence Agency (NGA). The monitoring stations are used to track the individual GPS SVs, monitor the navigation, range, and carrier signals, and collect atmospheric data. The information collected by the monitoring stations are then sent to the master control station to calculate precise SV locations, generate navigation messages, monitor satellite integrity, perform satellite maintenance, and provide command and control of the constellation. The main master control station operates out of air force base (AFB) Schriever in Colorado with a backup station at Vandenberg AFB in

California.

4.2.3 GPS User Segment

The GP user segment consists of the user’s GPS receiver and antenna. This segment receives the broadcast GP signals and computes the user’s precise PVT. To compute the user’s PVT in 3D space, four unknowns need to be solved for: , y, z, and δ푡.

Solving these four unknowns requires the use of four measurements, from a minimum of four different GPS SVs. The distance calculation between the receiver and each satellite are non-linear equations, and all four must be solved simultaneously using the following equations:

2 2 2 푃푅1 = √(푥 − 푥1) + (푦 − 푦1) + (푧 − 푧1) + 푐훿푡 2 2 2 푃푅2 = √(푥 − 푥2) + (푦 − 푦2) + (푧 − 푧2) + 푐δ푡 2 2 2 푃푅3 = √(푥 − 푥3) + (푦 − 푦3) + (푧 − 푧3) + 푐δ푡 (3) ⋮ 2 2 2 푃푅푛 = √(푥 − 푥푛) + (푦 − 푦푛) + (푧 − 푧푛) + 푐δ푡

where:

푃푅푛 is the pseudorange between the user and the n-th satellite 40

푥, 푦, 푥 is the user position

푥푛, 푦푛, 푧푛 is the n-th satellite position

푐 is the speed of light and

δ푡 is the user clock offset from GPS time

Through linearization, equation (3) can be rewritten as:

푥 − 푥푛 푦 − 푦푛 푧 − 푧푛 Δ푃푅푛 = Δ푥 + Δ푦 + Δ푧 + 푐훿푡 (4) 푅푛 푅푛 푅푛

Combining Δ푥, Δ푦, Δ푧, and 푐훿푡 into a column vector and rearranging equation (4) into a n by 4 matrix known as the measurement matrix (H) results in: 푥 − 푥 푦 − 푦 푧 − 푧 1 1 1 1 푅 푅 푅 1 1 1 Δ푥 푥 − 푥2 푦 − 푦2 푧 − 푧2 1 Δ푦 Δ푃푅 = 푅 푅 푅 [ ] = 퐇Δ퐱 (5) 2 2 2 Δ푧 ⋮ ⋮ ⋮ ⋮ 푥 − 푥 푦 − 푦 푧 − 푧 푐훿푡 푛 푛 푛 1 [ 푅푛 푅푛 푅푛 ]

The most common method to solve the user’s position is to use the Linearized Least

Squares (LLS) estimation method. The standard LLS equation is given by:

Δ퐱푎 = 퐒푎Δ퐲 (6)

where Δ푥푎 is the change in the user state, and Δ푦 is the measurement residual vector defined as:

Δ퐲 = 훒풎풆풂풔 − (훒풆풔풕 + 푐훿푡) (7)

41 where 𝝆풎풆풂풔 is the measured pseudorange of each SV, 𝝆풆풔풕 is the estimated pseudorange of each SV based on the current user estimate, and c is the current user clock offset estimate. Equation (6) can be expanded into

Δ퐱 = (퐇푇퐇)−1퐇푇Δ퐲 (8)

Equation (8) is then iteratively solved, updating the estimated user position through the change in the user state per iteration, until the change in the user state converges to zero or near zero.

Many position computation methods, such as the weighted least squares (WLS) method, and integrity calculations require the calculations of a weight matrix, 퐖. The standard weight matrix is given by:

1 2 0 0 … 0 σ1 1 0 0 … 0 2 1 1 1 1 σ2 퐖 = 푑𝑖푎푔 ( … ) = 1 (9) σ2 σ2 σ2 σ2 1 2 3 푛 0 0 2 … 0 σ3 ⋮ ⋮ ⋮ ⋱ ⋮ 1 0 0 0 … 2 [ σ푛]

th where 휎푛 is the n satellite’s standard deviation error statistic for its pseudorange measurement. The method for calculating 휎푛 is dependent on the navigation system being used and the current procedure. This is discussed further in chapters 7, 8, and 9, and

Appendix A, Appendix B, and Appendix C. 42

4.2.4 GPS Error Sources

The accuracy of the GPS range measurement, and in turn the accuracy of the position solution, can be reduced due to several sources of error. Reference [18] lists the errors sources accounted for in the L1 single-frequency error budget and groups them to their associated GPS segment. The error sources associated with the GPS space segment are clock stability, group delay stability, differential group delay stability satellite acceleration uncertainty, other space segment errors. The errors associated with the GPS control segment are clock/ephemeris estimation, clock/ephemeris prediction, clock/ephemeris curve fit, ionosphere delay model terms, group delay time correction, and other control segment errors. Finally, the error sources associated with the user are ionosphere delay, tropospheric delay, receiver noise and resolution, multipath, and other user segment errors.

4.3 Aircraft Based Augmentation Systems (ABAS)

An aircraft based augmentation system (ABAS) is on-board augmentation to GPS or other GNSS systems to improve the performance, specifically accuracy and integrity, of the GNSS system. Currently the most common ABAS implementation is receiver autonomous integrity monitoring (RAIM).

RAIM is an algorithm implemented by the user to detect a single satellite failure.

RAIM detects satellite failures by comparing the position solution using the full set of satellites that can be seen by the user to every subset of satellites with one satellite excluded. If a satellite failure exists that has a noticeable impact on the solution it will affect all subsets with that satellite in it, but not the subset with the failed satellite removed. 43

Depending on the available measurements RAIM can detect and possibly even remove the satellite failure from its position solution [19].

Advanced RAIM (ARAIM) builds on traditional RAIM and Fault Detection and

Exclusion (FDE) algorithms to include position and protection level computations for both lateral and vertical navigation by the addition of a second GPS frequency and/or a second

GNSS constellation [15]. This study uses the solution separation method for the ARAIM algorithm and is described further in Appendix D.

4.4 Satellite Based Augmentation System (SBAS)

SBAS are regional GNSS augmentation systems designed to improve the accuracy and integrity of GNSS navigation for civil aviation [11]. SBAS monitors the relevant

GNSS constellation using several ground monitoring stations across the target improvement area. The measurements are then processed at a central location, and corrections and integrity values are generated and relayed to satellites in geosynchronous equatorial orbit (GEO), also referred to as geostationary orbit. The GEO satellites then broadcast the calculated information to SBAS enabled GNSS receivers allowing them to improve their position solution. SBAS systems provide corrections for SV position errors, clock errors, as well as errors due to ionospheric delays.

In the United States, the FAA developed and maintains its own SBAS system,

WAAS, for GPS users in North and parts of South America. In Europe, the European Space

Agency (ESA), the European Commission, and Eurocontrol partnered to create the

European Geostationary Navigation Overlay Service (EGNOS) to augment GPS for

European aviation users. 44

Depending on the location of the user and the current availability, WAAS is able to provide users with the ability to perform a variety of procedures ranging from a RNP of 0.3 nmi to the ability to fly a APV LPV-200 approach down to a minimum of 200ft AGL [20].

WAAS was developed with the intended maximum capability of supporting an uncoupled14 LPV-200 approach. Therefore, the guaranteed accuracy of the system is the minimum accuracy required to complete an approach to a 200ft AGL minimum. According to the WAAS Performance Standard [16], the accuracy requirements for LPV-200 are a horizontal 95% accuracy of 16 meters and a vertical 95% accuracy of 4 meters. The actual

WAAS LPV-200 accuracy is much higher than the accuracy requirement set by the performance standard with a horizontal 95% accuracy of 0.7 meters and a vertical accuracy

95% of 1.2 meters [21]. Therefore, the lifetime vertical and horizontal accuracy of WAAS meets and exceeds the NSE requirements for full autoland operations, but it is not authorized for use as the sole navigation reference for this application due to integrity limitations placed on it during development [21].

4.4.1 Localizer Performance with Vertical Guidance (LPV) with WAAS

LPV is an APV with minima similar to CAT I, although instead of using ground based navaids, the aircraft’s position is calculated using GP augmented with BA , such as WAAS. The improvement to the GPS position accuracy and integrity through SBAS allows the aircraft to fly a simulated glideslope stored in its navigational database.

14 Manually flown by the flight crew 45

4.5 Ground Based Augmentation System Landing System (GLS)

GLS is a precision approach system with the intentions of providing approach service down to a DH of zero-foot. Currently there are only two publicly available GLS systems in the US, one at Newark Liberty International Airport (EWR) and one at George

Bush Intercontinental Airport (IAH) [22]. At the writing of this thesis, these systems are able to provide service equivalent to ILS Category (CAT) I.

GLS, based around GBAS, works by augmenting, or improving, GPS through the use of ground-based reference receivers placed at or near the airport. Four reference receivers with known locations receive the ranging signals provided by the GPS satellites.

Using the received pseudoranges, a central station calculates corrections to the pseudoranges and broadcasts the corrections to the approaching aircraft over a VHF data broadcast (VDB). Applying the broadcast corrections, the approaching aircraft is able to improve its calculated GPS position and fly a simulated glideslope to the touchdown point stored in a navigational database [10]. The typical GBAS architecture is represented in

Figure 4-2.

Figure 4-2: Typical GBAS Architecture [22]

As with ILS, GLS approach performance is broken down into 6 different categories, know and GBAS service levels (GSL). GSL performance are related back to the ILS CAT performance. Table 4-2 outlines the different GSLs and the associated DHs and RVRs.

Table 4-2: GBAS Service Levels for Approach Service (derived from [10]) GBAS Typical operation(s) which may Service be supported by this level of Decision Height RVR Level service Approach operations with vertical A - - guidance (APV-I) Approach operations with vertical B - - guidance (APV-II) Precision Approach to lowest C >200ft (60m) >1,800ft (550m) Category I minima Precision approach to lowest Category IIIb minima, when <50ft (15m) or no 150-700ft (50- D augmented with other airborne DH 200m) equipment Precision Approach to lowest <100ft (30m) or no E >700ft (200m) Category II/IIIa minima DH Precision approach to lowest <50ft (15m) or no 150-700ft (50- F Category IIIb minima DH 200m)

4.6 Radar Altimeter

Radar altimeters are widely used throughout commercial aviation for determining the aircraft’s height above ground level (AGL). The aircraft height AGL is vital for systems such as the Ground Proximity Warning Systems (GPWS) and the aircraft autopilot. The aircraft autopilot uses the radar altimeter, for example, for automatic flare maneuvers during IL CAT III landings. To calculate the aircraft’s height AGL, the radar altimeter measures the time it takes for a radio wave transmitted from one aircraft antenna on the aircraft to travel from that antenna to the terrain below the aircraft and back to a second antenna on the aircraft. This total travel time is then divided in half and multiplied by the speed of light to get the measured distance [23].

Two common types of radar altimeters are used in aviation to determine an aircraft’s height. The first type, frequency-modulated continuous-wave (FM-CW), are 48 more commonly found on civil aircraft. FM-CW radar altimeters operate by transmitting a constantly varying frequency signal and then measure the difference between the transmitted frequency and received frequency to determine the signals time of travel. The second type radar altimeter commonly used is the pulse modulation radar altimeter. Pulse modulation radar altimeters are commonly found on military aircraft and operate by transmitting a single radio wave pules at a certain pulse repetition frequency (PRF) and measures the time between the transmission and return pulse.

The specifications for radar altimeters for use in commercial aircraft autoland systems have been set by Aeronautical Radio, Incorporated (ARINC). Reference [24] states that a radar altimeter for use in autoland systems must operate between -20 and 2500 ft

AGL and have an accuracy of 1.5ft (0.457m) or 2% of the altitude, whichever is greater.

These requirements must be met when the aircraft’s bank angle ≤ 40° and a pitch angle ≤

20°. The specification does not give a requirement for the radar altimeter antenna beamwidth but leaves it up the manufacturer to choose the best beamwidth for the application. The half power or 3dB beamwidth of a radar altimeter antenna is illustrated in

Figure 4-3.

3dB Point 3dB Point

Figure 4-3: Radar Altimeter Beamwidth

Due to the height of the aircraft and the associated beamwidth of the antenna, radar altimeters do not only measure the distance of the ground directly below the aircraft, such as a laser altimeter would, but illuminates a large circular area below the aircraft. This illumination area is described further in Figure 4-4. The estimated illumination area can be calculated as [25]:

1 퐴 = 휋푟2 = 휋(ℎ2 tan2( θ )) (10) 푟푎푑 2 3dB

Figure 4-4: Radar Altimeter Illumination Zone

As a consequence of the illumination area, the height AGL provided by the radar altimeter may not always accurately represent the ground directly below the aircraft. This issue, and methods for compensating for it are discussed further in chapter 10 and [23].

Due to the method of measuring the distance to the terrain, FM-CW radar altimeters tend to provide an average height of the terrain in the beamwidth while pulse modulation radar altimeters tend to provide the height relative to the first return of the pulse [23]. 51

5 TERRAIN DATABASE BACKGROUND

5.1 World Geodetic System (WGS) 84 and Earth Gravitational Model (EGM) 96

In addition to coordinate frames, it is often essential to create a reference frame and geometric shape of the earth. Two common models are used to describe the shape of the earth. The first and most basic, the reference ellipsoid, is a mathematical representation and is created by directionally scaling a sphere to more closely match the oblate spheroid shape of the earth. The most common reference ellipsoid used in navigation is the World

Geodetic System (WGS) 84 and can be calculated using four values, the equatorial radius

(푎), the earths flattening (푓), the earths gravitational constant (퐺푀), and the earths angular velocity (휔) [25].

The second commonly used shape to represent the earth is a reference geoid. A reference geoid is designed to represent the shape of the oceans’ surface if only influenced by the earth’s gravity and rotation. This shape often represents zero mean sea level (MSL) at a given location. The most commonly used geoid is the Earth Gravitational Model

(EGM) 96 developed by NGA and is defined by spherical harmonic coefficients. While the geoid can be calculated at any point on earth using the spherical harmonic coefficients, it is more common to interpolate the geoid height from a raster database of the geoid [25].

Most GNSS system, specifically GPS, are referenced to the WGS84 ellipsoid whereas many terrain elevation databases are referenced to MSL through the EGM96 geoid

[25]. Therefore, the height difference from the WGS84 ellipsoid to the geoid, known as geoid undulation, is needed. The relationship between the WGS84 ellipsoid, geoid, and 52 terrain height are depicted in Figure 5-1. To calculate the approximate height above the

WGS84 ellipsoid, known as the ellipsoidal height (ℎ), the following equation is used:

ℎ = 퐻 + 푁 (11)

where

퐻 is the Orthometric Height (height above the geoid/MSL)

푁 is the Geoidal Height (geoid undulation)

Given the correct information and databases, the craft’s and/or terrain height above the WGS84 ellipsoid or EGM96 geoid can be calculated at any point on the earth.

Figure 5-1: Geoid Undulation

5.2 Digital Elevation Models (DEM)

Terrain elevation databases or DEM provide information on the elevation of the terrain around the aircraft through bilinear interpolation of elevations at discrete recorded points. The interpolation of the terrain height from discrete recorded points is illustrated in

Figure 5-2.

h(0,1)

h h( ,y) (1,1)

h(0,0)

(0,1) h(1,0) (1,1) (0,0) (1,0)

Figure 5-2: Terrain Database Interpolation

Terrain databases are generated from various sources, the most common being radar data or Light Detection and Ranging (LiDAR) data. Each method has its respective advantages and disadvantages. Radar based maps are generated with a system that is typically in the same or nearby frequency band as the radar altimeter, these maps tend to be more representative of the terrain that is detected by a radar altimeter return. If synthetic aperture radar (SAR) methods are used, the generated map may contain more information than is typically observed by a radar altimeter. Alternatively, LiDAR based maps may include more detail (e.g. foliage) that is not observed by a radar. LiDAR generated maps tend to have higher resolution when compared to a radar generated map and often have a better point to point accuracy between those points.

For the work presented in this thesis, two radar and two LiDAR generated terrain elevation maps were used for the analysis. The first radar generated database is a product of (NGA called Digital Terrain Elevation Data Level 1 (DTED1) [26]. The second radar 54 terrain elevation database used in this study was created from the National Aeronautics and

Space Administration (NASA) Shuttle Radar Topography Mission (SRTM) during Space

Transportation System (STS) 99 onboard Space Shuttle Orbiter Endeavour. The two different LiDAR generated elevation maps were used in this study, one for the tests conducted at the Ohio University airport (KUNI) and another for the tests conducted at the

Reno-Tahoe International Airport (KRNO).

5.3 Digital Terrain Elevation Data (DTED)

DTED is a terrain database product and a database standard developed by the NGA initially for military applications, specifically radar altimeter simulations. For military reason, the DTED databases only provided the height of terrain and remove manmade structure such as buildings, roads, and bridges. Currently, there exists three different DTED standards, DTED Level 0, Level 1, and Level 2. The DTED level refers to the post spacing and accuracy of the database. Table 5-1 lists the different DTED levels and their associated post spacing, absolute vertical accuracy, absolute horizontal accuracy, and relative vertical accuracy, all accuracy values are 90%.

Table 5-1: DTED Specifications [26] Post Spacing Absolute Absolute Relative DTED (Lat & Lon Vertical Horizontal Vertical arc seconds) Accuracy Accuracy Accuracy Level 0 30 < 30m L.E.15 < 50m C.E.16 < 20m L.E. Level 1 3 < 30m L.E. < 50m C.E. < 20m L.E. < 12m L.E. (0-20% slope) Level 2 1 < 18m L.E. < 23m C.E. < 15 L.E. (>20% slope)

For this study an unclassified version of DTED1 was used. The DTED level 1 specification states a relative (point-to-point) horizontal error of 23.03 meters with a 90% confidence and a relative vertical error of 20 meters with a confidence of 90%. Table 5-2 below summarizes all relevant DTED level 1 accuracy specifications in terms of distribution, mean, and variance assuming a normal distribution.

Table 5-2: DTED1 Accuracy Specifications [26] Vertical Horizontal Absolute Error 풩(0, 18.22) 풩(0, 2 . 2) Relative Error 풩(0, 12.12) 풩(0, 14.02)

DTED1 provides elevation data every three (3) arc seconds, approximately 90 meters in the areas tested in this thesis, relative to EGM96.

5.4 Shuttle Radar Topography Mission (STRM)

STRM was a joint effort between NASA and NGA to create a more complete and accurate terrain elevation database of the globe. Unlike the unclassified versions of the

15 Linear Error 16 Circular Error 56

DTED database, the SRTM database includes manmade structures in its data. Table 5-3 lists the SRTM absolute geolocation error, absolute height error, and relative height error for each geographic body, all values are 90%.

Table 5-3: SRTM Specifications [27] North South Africa Australia Eurasia Islands America America Absolute Geolocation 11.9m 7.2m 8.8m 9.0m 12.6m 9.0m Error Absolute Height 5.6m 6.0m 6.2m 8.0m 9.0m 6.2m Error Relative Height 9.8m 4.7m 8.7m 6.2m 7.0m 5.5m Error

The SRTM specification states a relative height error in North America of 7.0 meters with 90% confidence and a relative height error of 8.7 meters with a 90% confidence in Eurasia. Table 5-4 summarizes all relevant SRTM accuracies in terms of distribution, mean, and variance.

Table 5-4: Localized SRTM Specifications [27] North America Eurasia Absolute Geolocation Error 풩(0, 7.652) 풩(0, 5. 42) Absolute Height Error 풩(0, 5.472) 풩(0, .672) Relative Height Error 풩(0, 4.252) 풩(0, 5.282)

The SRTM terrain database provides elevation data every one (1) arc seconds, approximately 30 meters in test areas, relative to EGM96. 57

5.5 LiDAR Generated Terrain Elevation Databases

The LiDAR map used at KUNI was provided by [28] and has an average post spacing of 2.0 meters with non-even spacing between each point. The LiDAR map used at

KRNO was provided by [29] and has an average post spacing 2.0 meters with non-even spacing between each point. For the purpose of this study, the relative height error for the

LiDAR databases was assumed to be 1.0 meters.

5.6 Terrain Database Integrity

Terrain elevation databases are essential components of multiple safety systems found on modern transport category aircraft such as the GPWS and enhanced GPWS. Due to the role that terrain elevation databases have in the safe navigation of aircraft, the accuracy and integrity of the terrain data inside the database is important. For this reason, multiple standards have been created to regulate the creation, distribution, and updating of terrain elevation databases. Some of these regulations include ICAO Annex 15

Aeronautical Information Services (ref. [13]), RTCA DO-200 Standards for Processing

Aeronautical Data (ref. [30]), RTCA DO-272 User Requirements for Aerodrome Mapping

Information (ref. [31]), and RTCA DO-276 User Requirements for Terrain and Obstacle

Data (ref. [32]). These documents define, among other things, the accuracy, resolution, confidence, and integrity of the included dataset based on the impact of safety. ICAO

Annex 15 defines three types of data based on the risk resulting from the use of corrupted data [13]: 58

a. Routine Data: “There is a very low probability when using corrupted routine

data that the continued safe flight and landing of an aircraft would be severely at

risk with the potential for catastrophe” [13]

b. Essential Data: “There is a low probability when using corrupted essential data

that the continued safe flight and landing of an aircraft would be severely at risk

with the potential for catastrophe” [13]

c. Critical Data: “There is a high probability when using corrupted critical data that

the continued safe flight and landing of an aircraft would be severely at risk with

the potential for catastrophe” [13]

Although not currently standardized, multiple systems have been proposed to create a real-time terrain database monitor using methods similar to terrain referenced navigation

(TRN). Currently, no terrain database has been shown to meet the highest level of data integrity on its own. To meet this level of integrity, a new database would need to be created in the areas of interest or a currently available terrain database augmented with an integrity monitor could be used. One integrity monitor, first proposed in [25] and built upon in [23] and [33], is the downward looking terrain database monitor (DLIM). With DLIM, terrain database integrity it checked in real-time by comparing the synthesized terrain sensed by the aircrafts radar altimeter to the terrain database terrain profile. This method would allow for less stringent requirements during the creation of the terrain database but introduce a real-time integrity monitor to mark the database as unavailable for a selected application if a discrepancy was found. 59

5.7 Terrain Referenced Navigation (TRN)

The RALT Aiding method proposed in this thesis is not a TRN system, although many of the concepts RALT Aiding builds on were originally conceived in TRN research.

In a TRN system, an aircraft calculates the height of the terrain above MSL by calculating the difference between the aircraft’s barometric altitude and radar altitude as it passes over terrain. These measurements are then recorded and time tagged to create a height profile of the terrain below the flight path of the aircraft. This sensed terrain profile is continuously updated and cross correlated with a terrain elevation database to determine the position of the aircraft [25]. While rarely used on manned aircraft, TRN systems are commonly found in military munitions and can achieve a lateral position error less than 31 meters [25].

6 RADAR ALTIMETER AIDING (RALT AIDING)

The goal of the work described in this thesis is to augment GPS with a radar altimeter and a local terrain database to enable precision approach and landing of RPAs without the need for ground based navigation aids. There exist multiple ways to integrate the radar altimeter measurement into the aircraft’s navigation solution. The first option is to use the radar altimeter measurement to verify the vertical GNSS position solution. This implementation would allow the system to ensure that the vertical component of the position solution is correct but may not provide enough information about the integrity of the horizontal component. Additionally, if there were a disagreement between the radar altimeter measurement and the GNSS vertical component, the radar altimeter would be unable to improve the solution, just mark it as inaccurate. A second implementation is to use the radar altimeter measurement as the “true” height of the aircraft and no longer use the G system to solve for the aircraft’s height. This solution would provide a highly accurate vertical position solution, but the improvements introduced by the radar altimeter would not directly improve the horizontal position solution, although some lateral improvement would be seen in the GPS position solution as the GPS system would no longer need to solve for height. A third possible approach would use the radar altimeter for terrain referenced navigation. While this solution has been used in military munitions and aircraft, certification for civil aircraft would require a regularly updated, highly accurate terrain database. A fourth option for augmenting GNSS with a radar altimeter is to include the radar altimeter measurement into the G ’s measurement equation. This architecture is discussed here and referred to as RALT Aiding. 61

The RALT Aiding method combines three independent subsystems together to increase the accuracy, integrity, and continuity of the navigation systems, allowing it to meet the navigational requirements of a CAT IIIb precision approach and landing without the use of ground based navigational aids. The three subsystems that combine to create the

RALT Aiding architecture are: (i) the selected GNSS system; either dual frequency GPS or GPS in conjunction with SBAS (i.e. WAAS), (ii) the onboard radar altimeter unit that provides a measurement of the aircraft’s height above ground level (AGL) for use in the position computation, (iii) and the terrain database used to determine the aircraft’s height above the WGS 84 ellipsoid given its height AGL. A high-level overview of these subsystems and their interactions are pictured in Figure 6-1.

Figure 6-1: RALT Aiding Architecture

RALT Aiding is being developed to enable an aircraft to fly a CAT IIIb autoland without the use of any ground-based navigational aids, for this, the systems is being designed to meet the navigational requirements of a GSL F system as defined in [10]. These requirements state that the systems must have a lateral NSE accuracy 95% of 5.0m, a vertical NSE accuracy 95% of 2.9m, a HAL of 17.0m and a VAL of 10.0m. Further requirements for GLS F and other approach systems has been provided in chapter 3.4, and is presented again in Table 6-1 below for GLS F.

Table 6-1: GLS F Navigational Requirements Accuracy Integrity Continuity Lateral Vertical Time Horizontal Vertical NSE NSE Integrity Continuity Approach to Alert Alert Accuracy Accuracy Probability Probability alert Limit Limit 95% 95% 1-2x10-6 in 1-1x10-9 in any 15 sec GSL F any 15 sec vertical, 1- 5.0 m 2.9 m 2 s 17.0 m 10.0 m [10] vertical, 30 2x10-6 for sec lateral any 30 sec lateral

6.1 System Description and Concept of Operation

RALT Aiding augments the standard GPS position solution through the addition of a range measurement provided by the radar altimeter and terrain elevation database combination. The RALT Aiding system preforms the following steps:

Step 1: The navigation system determines its best position estimate using the unaugmented

selected navigation system, either dual-frequency GPS or GPS augmented with

SBAS, and its previous augmented position estimate. 63

Step 2: The aircraft’s height AGL is determined by the radar altimeter.

Step 3: The height of the terrain above the WGS 84 ellipsoid is determined through the use

of a terrain elevation database and the unaugmented position estimate.

Step 4: The radar altimeter height AGL measurement and the terrain elevation height are

combined to calculate the radar altimeter antenna height above the WGS 84

ellipsoid.

Step 5: Additional corrections are applied to the radar range measurement, including a

correction for the difference between the radar altimeter antenna and GPS antenna,

corrections for the pitch and roll of the aircraft, and possible corrections for the

terrain elevation database, to determine the GP antenna’s height above the WG

84 ellipsoid through the use of the radar altimeter. The combination of

measurements and corrections used to compute the corrected radar altimeter

measurement are illustrated in Figure 6-2. It should be noted that the terrain

databased used in the system could be directly referenced to the WGS84 ellipsoid

or referenced to the EGM86 geoid. If the latter is used, an additional database is

required to convert the EGM86 refenced height to WGS84 referenced height. This

two-step conversion is shown in Figure 6-2.

GP Height

Corrected RALT RALT Height easurement Height Difference of GP and RALT Antennas

RALT easured Height

Terrain Height Terrain Database Value G Geoid ( L) WG / G Correction

WG 4 llipsoid

Figure 6-2: Radar Altimeter Derived Range Height Corrections

Step 6: The GPS antenna height above the WGS 84 ellipsoid computed from the radar

altimeter measurement is converted to a range measurement similar to that of a GPS

pseudorange measurement.

Step 7: The additional range measurement is introduced into the standard GPS LLS

equation for computing the aircrafts position. This additional range measurement

improves the accuracy, integrity, and continuity of the GPS position measurement,

and has other major benefits such as introducing a range measurement with a large

geometric diversity when compared the GPS SV range measurements.

The intended operational use of the systems is during the final leg of a precision approach into an airport, from ≤ 00f t above the runway threshold to touchdown and rollout. At a standard 3-degree glideslope, the along track operational range of the RALT 65

Aiding systems is around 3,000 meters or 1.6 nmi. This operational range is selected for multiple reasons. Firstly, it is expected that other navigation systems such as SBAS or dual frequency GPS alone will allow for an RPA to complete a precision approach to ~200ft above the runway threshold [34]. Secondly, the performance of the RALT vertical position solution is expected to be best below this height threshold, given that the ranging error of most radar altimeter units available for commercial aviation is a function of height AGL, with a smaller error for lower ranges. Also, given the runway safety area (RSA), runway object free area (ROFA), runway obstacle free zone (ROFZ), precision obstacle free zone

(POFZ), approach runway protection zone (RPZ), and departure runway protection zone requirements defined by the FAA in AC 150/5300-13A Airport Design (ref. [35]), most runways have a guaranteed obstacle free zone on the approach path where the radar altimeter is expected to accurately measure the height AGL This operational range is compared to other approach systems operational range in Figure 6-3.

Figure 6-3: RALT Aiding Concept Operational Range

6.2 RALT Aiding Method

RALT Aiding provides augmentation to GPS through the combination of several navigation systems. The methods for the craft’s position estimation and integrity assessment are described in the following sections.

6.3 Position Computation

RALT Aiding computes the user position solution though a modified version of the

LLS method of the method described in chapter 4.2.3. The LLS method is modified to include the additional range measurement provided by the combination of the radar altimeter and terrain elevation database. Following the acquisition of the user position using the unaugmented GPS system, the estimated height of the terrain can be determined through a terrain database lookup. This estimated terrain height (including corrections) is added to the aircraft’s height AGL provided by the radar altimeter (including corrections) 67 to produce the new range measurement to be used in the LLS algorithm. This computation

푅퐴퐿푇 of the range measurement, 휌푚푒푎푠, is given by:

푅퐴퐿푇 휌푚푒푎푠 = ℎ푟푎푑 + ℎ푑푒푚 = ℎ푎푔푙 + 푓(푙푎푡, 푙표푛) (12)

where ℎ푎푔푙 is the height of the aircraft’s GP antenna above ground level with corrections and 푓(푙푎푡, 푙표푛) represents the terrain database lookup with any included corrections.

To include the additional range measurement into the standard LLS equations given by:

Δ퐱 = (퐇푇퐇)−1퐇푻Δ퐲 (13)

modifications to the linearized measurement matrix, 퐇, and the measurement residual vector, Δ퐲, are made. First, and additional row is introduced into the 퐇 matrix to include the line-of-sight vector from the aircraft’s GP antenna to the terrain data directly below the aircraft. The modified 퐇 matrix is now given by:

푥 − 푥1 푦 − 푦1 푧 − 푧1 1 푅1 푅1 푅1 푥 − 푥 푦 − 푦 푧 − 푧 2 2 2 1 푅2 푅2 푅2 퐇푛 = (14) ⋮ ⋮ ⋮ ⋮ 푥 − 푥 푦 − 푦 푧 − 푧 푛 푛 푛 1 푅푛 푅푛 푅푛 [ 0 0 1 0]

For the RALT Aiding method, the 퐇 matrix is calculated in a locally level coordinate frame, such as ENU, relative to the aircraft. Given that the radar altimeter derived measurement only provides information in the vertical domain, its line-of-sight 68 vector can simply be represented with a one (1) in the Up direction and zeros (0) in the horizontal directions. The remainder of the 퐇 matrix remains unchanged with 푥, 푦, 푧 being

th the current user position estimate, 푥푛, 푦푛, 푧푛, being the position of the n satellite, and 푅푛 being the estimated pseudorange between the user and nth satellite, all expressed in the

ENU coordinate frame.

To modify the standard Δ퐲 equation, shown below and found in chapter 4.2.3, for

RALT Aiding, the measured pseudorange matrix, 훒푚푒푎푠, and estimated pseudorange matrix, 훒푒푠푡, are expanded to include the radar altimeter derived range measurement:

Δ퐲 = 훒풎풆풂풔 − (훒풆풔풕 + 푐훿푡) (15)

1 2 3 푛 푅퐴퐿푇 푻 훒푒푠푡 = [휌푒푠푡 휌푒푠푡 휌푒푠푡 … 휌푒푠푡 휌푒푠푡 ] (16)

1 2 3 푛 푅퐴퐿푇 푻 훒푒푠푡 = [휌푒푠푡 휌푒푠푡 휌푒푠푡 … 휌푒푠푡 휌푒푠푡 ] (17)

It should be noted that the user calculated clock offset, 퐜훿푡, in the Δ퐲 equation is

푅퐴퐿푇 not applied to 훒푒푠푡 , only to the satellite pseudorange estimated measurement. Following these modifications, the rest of the LLS method for computing the user’s position remains unchanged.

6.3.1 Integrity Computation

To compute the user protection levels, HPL and VPL, RALT Aiding uses either a modified version of the WAAS integrity calculations or a modified version of the RAIM solution separation method, depending on the selected GNSS system to be augmented with

RALT Aiding. The standard weight matrix, 퐖, as defined in chapter 4.2.3, is modified to 69 include an additional row and column to account for the standard deviation associated with radar altimeter derived range measurement. Thus, the modified 퐖 matrix is given by:

1 1 1 1 1 퐖 = 푑𝑖푎푔 ( 2 2 2 … 2 2 ) σ1 σ2 σ3 σ푛 σ푅퐴퐿푇 1 2 0 0 … 0 0 σ1 1 0 0 … 0 0 σ2 2 (18 1 0 0 … 0 0 ) = 2 σ3 ⋮ ⋮ ⋮ ⋱ ⋮ ⋮ 1 0 0 0 … 0 σ2 푛 1 0 0 0 … 0 2 [ σ푅퐴퐿푇 ]

where 휎푛 is the standard deviation error statistic for the n-th GPS satellite pseudorange measurement as discussed in Appendix A, Appendix B, and Appendix C, and 휎푅퐴퐿푇 is the standard deviation error statistic for the radar altimeter derived range measurement. It should be noted that the modification to the 퐖 matrix is made to both the nominal weight matrix, 퐖푛표푚, and the maximum weight matrix, 퐖푚푎푥, when using the RAIM Solution

Separation method to calculate HPL and VPL. The calculated value to be used for 휎푅퐴퐿푇 is the same in both 퐖푛표푚 and 퐖푚푎푥.

The standard deviation error statistics for the radar altimeter derived range measurement, 휎푅퐴퐿푇, is taken to be the root sum square of all errors associated with the radar altimeter and terrain elevation database:

2 2 2 σ푅퐴퐿푇 = √σ푠푡푎푡푖푐 + σ푎푙푡 + σ푡푒푟푟푎푖푛 (19)

70 where 휎푠푡푎푡푖푐 is the nominal error of the RALT unit, 휎푎푙푡 is the error associated with the additional measurement error due to the RALT height above the ground and changes throughout the approach, and 휎푡푒푟푟푎푖푛 is the error associated with the terrain database. In most studies completed so far, the values for 휎푠푡푎푡푖푐 and 휎푎푙푡 have been derived from the minimum specifications listed in [24]. While these values would change dependent of the radar altimeter being used, [24] requires a radar altimeter that is being used in commercial aviation to have an accuracy of ±1.5 feet (0.46m) or 2% of true altitude, whichever is greater. Since no bound is given on these values and they are stated as a maximum accuracy error, they have been interpreted as 3-sigma values for use in the radar altimeter derived range error statistic. While the stated specification in [24] is ±1.5 feet (0.46m) or 2% of true altitude, whichever is greater, both values are included in the error statistic calculation to ensure that the error statistic is overbounded. If the accuracy values listed in [24] are used, 휎푠푡푎푡푖푐 and 휎푎푙푡, are calculated using the following equations:

휎푠푡푎푡푖푐 = 0.152 (20)

휎푎푙푡 = 0.066 × 푇푟푢푒퐴퐿퐺 (21)

The final error statistic in the RALT Aiding range measurement error statistic is associated with the terrain elevation database post-to-post vertical error. This value is selected based on the terrain elevation database used, such as DTED or SRTM (see chapter

5.2), or may require additional calculations as discussed further in chapter 9.

Following the computation of the weight matrix for RALT Aiding, the HPL and

VPL if using WAAS as the primary GNSS system can be calculated using: 71

푉푃퐿푊퐴퐴푆 = 퐾푉 ∗ 푑푈 (22)

퐻푃퐿푊퐴퐴푆 = 퐾퐻,푃퐴 ∗ 푑푚푎푗표푟 (23)

If single- or dual-frequency GPS is selected as the primary GNSS system, the HPL and VPL are calculated as:

퐻푃퐿푓푎푢푙푡푒푑 = 푚푎푥(퐻푃퐿푛) (24)

푉푃퐿푓푎푢푙푡푒푑 = 푚푎푥(푉푃퐿푛) (25)

where 퐻푃퐿푛 and 푉푃퐿푛 are calculated as:

퐻푃퐿 = 퐷 + 퐾 ∗ σ + ∑ √푆2 (1, 𝑖) + 푆2 (2, 𝑖) 푛 푑퐻,푛 푚푑,푛 퐻,푛 푛,푚푎푥 푛,푚푎푥 (26) ∗ 푏푚푎푥,푖

푉푃퐿푛 = 퐷푑푉,푛 + 퐾푚푑,푛 ∗ σ푉,푛 + ∑|푆푛,푚푎푥( , 𝑖)| ∗ 푏푚푎푥,푖 (27)

The complete set of equations and derivations for WAAS HPL and VPL can be found in Appendix E and for single- or dual-frequency GPS in Appendix D.

7 FEASIBILITY STUDY

Following the development of the RALT Aiding architecture, this chapter proposes a simulation and evaluates the feasibility of the system to meet the GSL F navigational requirements. The simulation has been designed to evaluate the performance of six different GNSS configurations, five different radar altimeter and terrain elevation database configurations, and three different GPS constellation configurations, for a total of 90 different simulation cases. The results of these simulation cases are then used to determine the minimum performance of the RALT Aiding subsystems for further development.

7.1 Simulation Method

This study considers six different GNSS configurations: (i) single frequency GPS,

(ii) GPS augmented with WAAS (i.e. SBAS), and (iii-vi) four dual-frequency GPS cases.

In the case of the dual-frequency GPS simulation, GPS L1 and L5 frequency bands have been used as they are within the protected aeronautical radionavigation service (ARNS) band. Within each of these six GNSS configurations, five different radar altimeter and terrain database configurations were used: (i) no radar altimeter, (ii) radar altimeter with a standard deviation of 12.1m for the terrain elevation database post-to-post vertical error,

(iii) radar altimeter with standard deviation of 5.28m of the terrain elevation database post- to-post vertical error, (iv) radar altimeter with standard deviation of 1.0m terrain elevation database vertical error, (v) and, finally, a radar altimeter with a terrain database vertical error standard deviation of 0.5m. These values were selected as 12.1m and 5.28m are the performance specification for DTED1 and SRTM specifications, while 1.0 and 0.5m were selected as theoretical databases that could be developed for RALT Aiding as derived from 73

LiDAR data if that would be necessary. Finally, in each of the previously listed configurations, three different GNSS constellation configurations were tested: (i) a 24 SV constellation based on [11], (ii) a 24+3 (27) SV constellation based on [18], and (iii) a 24+7

(31) SV constellation that was in orbit on January 1st, 2019. When combined, a total of 90 different cases have been simulated in this study.

Each of the 90 test cases have been simulated across a 24-hour period, selected as the GPS constellation repeats every sidereal day, to determine the lifetime performance of

RALT Aiding with that constellation. During the 24-hour simulation period, the vertical accuracy, horizontal accuracy, VPL, and HPL has been calculated once per minute for a total of 1440 samples. The simulation was run at a fixed point at a simulated height of

200m above the KUNI airport runway. The simulation applies a 5-degree elevation mask as to not include GPS SV near the horizon. Each GPS SV has an individually calculated pseudorange error statistic to be used in calculating the error in the user’s position estimate, and the radar altimeter measurement error is calculated based on its height AGL and terrain database being used.

7.1.1 Selected Error Statistics

The methods used to calculate the error statistics and weight matrix for use in the position accuracy, HPL, and VPL calculations are defined in Appendix D and Appendix

E. For this offline simulation, certain error statistics must be selected ahead of time for input to the algorithm. 74

For single-frequency GPS the values for User Range Error17 (URE) and User

Equipment Error18 (UEE) must be selected. The selected value for URE and UEE are defined in [18] and are assumed to be the same for each SV and independent of elevation angle. The selected values for URE and UEE are presented in Table 7-1.

Table 7-1: Single Frequency SV Errors [2] Error Sources Value User Range Error (URE) 12.8 meters 95% User Equipment Error 5.5 meters 95% (UEE)

For dual-frequency GPS, the values for User Range Accuracy19 (URA) and URE are selected. As previously discussed, four different dual-frequency GPS cases are being examined, which relate to four different values sets for URA and URE. The four different

URA and URE values were selected to determine the over-bounded, actual, and future dual-frequency GPS performance. The selected values are presented in Table 7-2.

Table 7-2: Dual Frequency GPS URA & URE Case URA URE Rationale 1 2.95 meters 2.95 meters GPS System-In-Space Range Error Gaussian Over bound [36] 2 2.4 meters 1.2 meters Commonly Observed GPS Values [37] 3 1 meter 0.5 meter GPS Evolutionary Architecture Study (GEAS) Phase I Assumption [37] 4 0.5 meter 0.25 meter Future Projection [37]

17 Pseudorange inaccuracy due to the system in space [18] 18 Pseudorange inaccuracy due to the receiver [18] 19 A conservative representation of each satellite’s e pe cted rms UR performance based on historical data [18] 75

2 2 For WAAS, values for 휎푈퐷푅퐸 , 훿푈퐷푅퐸, 푐표푛푠푡푎푛푡 푡푒푟푚, 휎푈퐼푉퐸, 휎푑푖푣푔, and 휎푛표푖푠푒, are selected ahead of time in a simulation environment. Table 7-3 lists the selected values used in this simulation and their rationale.

Table 7-3: Selected WAAS Simulation Values Variable Selected Value Rationale 2 휎푈퐷푅퐸 1.2992 Commonly seen value based on [38] 훿푈퐷푅퐸 1 From [11] 푐표푛푠푡푎푛푡 푡푒푟푚 0 From [11] 2 휎푈퐼푉퐸 1.1974 Commonly seen value based on [38] 2 2 √휎푑푖푣푔 + 휎푛표푖푠푒 0.36 meters Worst case values defined in [11]

The radar altimeter modeled in this study was a Honeywell ALA-52B. This radar altimeter has a listed accuracy of ±1.5 feet (0.46m) or 2% of true altitude, whichever is greater [39], and is interpreted as a 3-sigma value. This radar altimeter was selected as it represents a common commercially available radar altimeter that is ARINC 707 (an aviation radar altimeter standard) compliant. This simulation assumes that the radar altimeter measures the ground directly below the aircraft and that the aircraft is not commanding any large pitch or roll maneuvers during a precision approach. These assumptions are investigated further in chapter 9.1.3.

The two error statistics associated with the radar altimeter, 휎푠푡푎푡푖푐 and 휎푎푙푡, are calculated using the following equations:

휎푠푡푎푡푖푐 = 0.152 (28)

휎푎푙푡 = 0.066 × 푇푟푢푒퐴퐿퐺 (29)

The final selected error statistic, 휎푡푒푟푟푎푖푛, is associated to the terrain elevation vertical error. This study simulates the accuracy listed in the DTED1 and SRTM specifications document discussed in chapter 5.2, as well as two theoretical database values. Table 7-4 lists the selected 휎푡푒푟푟푎푖푛 values used in this simulation.

Table 7-4: Simulated Terrain Database Error Statistics

Database 𝝈풕풆풓풓풂풊풏 Value DTED1 12.1 meters SRTM 5.28 meters Theoretical A 1 meter Theoretical B 0.5 meter

7.1.2 Accuracy Calculation Algorithm

To calculate the 95% and 99.99999% accuracy bounds in this simulation, a modified version of the all-in-view linearized WLS GPS position calculation was used.

The standard expanded WLS equation is given by:

푇 −1 푇 Δ퐱 = (퐇 퐖푛표푚퐇) 퐇 퐖푛표푚Δ퐲 (30)

where Δ푥 is the change in the user state, Δ퐲 is the measurement residual vector, and 퐇 is the measurement matrix as defined in chapter 4.2.3. 퐖푛표푚 is the weight matrix s defined in chapter 4.2.3 using 휎푖,푛표푚 as defined in Appendix B. The 95% and 99.99999% fault free vertical accuracy bounds are calculated by:

푉95% = 1.96휎푣,푛표푚 (31)

푉99.99999% = 4.89휎푣,푛표푚 (32)

77 where the vertical standard deviation, 휎푣,푛표푚, is given by:

휎푣,푛표푚 = √퐏푎,푛표푚( , ) (33)

and 퐏푎,푛표푚 is the all-in-view covariance matrix calculated as follows:

푇 −1 퐏푎,푛표푚 = (퐇 퐖푛표푚퐇) (34)

In a similar fashion to the vertical error bounds, the horizontal fault free 95% and

99.99999% error bounds are calculated as:

퐻95% = 1.96휎ℎ,푛표푚 (35)

퐻99.99999% = 4.89휎ℎ,푛표푚 (36)

where 휎ℎ,푛표푚 is the semi-major axis of the horizontal noise error ellipse and is calculated by:

1 휎 = √ (σ2 + σ2 + √(σ2 − σ2)2 + 4σ2 ) (37) ℎ,푛표푚 2 1 2 1 2 12

2 2 The variances 휎1 , 휎2 , and 휎12 come from the all-in-view covariance matrix as shown below:

2 σ1 σ12 σ13 σ14 2 σ21 σ2 σ23 σ24 퐏푎,푛표푚 = 2 (38) σ31 σ32 σ3 σ34 2 (σ41 σ42 σ43 σ4 )

7.1.3 Protection Level Calculation Algorithm

To calculate the HPL and VPL for RALT Aiding when using single- or dual- frequency GPS the following equations were used:

퐻푃퐿푓푎푢푙푡푒푑 = 푚푎푥(퐻푃퐿푛) (39)

푉푃퐿푓푎푢푙푡푒푑 = 푚푎푥(푉푃퐿푛) (40)

where 퐻푃퐿푛 and 푉푃퐿푛 are calculated as:

퐻푃퐿 = 퐷 + 퐾 ∗ σ + ∑ √푆2 (1, 𝑖) + 푆2 (2, 𝑖) 푛 푑퐻,푛 푚푑,푛 퐻,푛 푛,푚푎푥 푛,푚푎푥 (41) ∗ 푏푚푎푥,푖

푉푃퐿푛 = 퐷푑푉,푛 + 퐾푚푑,푛 ∗ σ푉,푛 + ∑|푆푛,푚푎푥( , 𝑖)| ∗ 푏푚푎푥,푖 (42)

The HPL and VPL calculations for RALT Aiding when using GPS augmented with

WAAS are:

푉푃퐿푊퐴퐴푆 = 퐾푉 ∗ 푑푈 (43)

퐻푃퐿푊퐴퐴푆 = 퐾퐻,푃퐴 ∗ 푑푚푎푗표푟 (44)

The full set of HPL and VPL equations plus their derivations can be found in

Appendix D and Appendix E for single- or dual-frequency GPS and WAAS respectively.

7.2 Feasibility Results

Table 7-5 through Table 7-10 present the results of the feasibility study in terms of availability throughout the 24-hour simulation period. Any results that meet the GLS F requirements for greater than or equal to 99.0% of the simulated time are highlighted in green. Given that the sample period is one minute, an unavailability of 1% relates to a total 79 downtime of just under 15 minutes in one day. It should be noted that in cases where the daily availability is above 99% but below 100%, the unavailability is spread out in small increments throughout the day. With this system the availabilities can be predicted ahead of time and landing operations can be scheduled around them.

Figure 7-1 presents the HPL and VPL for dual frequency GPS case #3 without

RALT aiding (result ID 61 in Table 7-9) across one day.

Figure 7-1: HPL & VPL for Dual Frequency Case #3 Without RALT Aiding (result ID 61)

The HPL has a daily availability of 96.6% and Figure 7-1 shows that at three different points in time the HPL exceeds the requirement. Additionally, just after 11 hours into the day there is a point where not enough SVs are in view to calculate an HPL or VPL value. Figure 7-2 presents the same case but with RALT aiding and a terrain database error of σ = 1.0m (result ID 70 in Table 7-9). Not only does the RALT have a major impact on the overall HPL and VPL, the addition of the RALT into the GNSS measurement matrix allowed for an HPL and VPL value to be calculated where the non-RALT aided solution did not have enough SVs for a protection level calculation.

Figure 7-2: HPL & VPL for Dual Frequency Case #3 Without RALT Aiding (σ = 1.0m) (result ID 70)

Figure 7-3 and Figure 7-4 present the same simulation case as in Figure 7-2, but displays the results in a format that is commonly referred to as the Stanford Diagram.

Figure 7-3: 2D Histogram of Vertical Error and VPL for Dual Frequency Case #3 with RALT Aiding (σ = 1.0m) (result ID 70)

This diagram plots position error along the x-axis and the protection level along the y-axis. For the system to support CAT IIIb operations, the plotted point must fall in the white triangle titled CAT IIIb Operational. If the plotted point falls within the regions titled

System Unavailable, the system has determined, based on comparing the HPL or VPL values to their respective HAL and VAL, that it is unable to perform a precision approach and landing. If the plotted point falls within the misleading information (MI) section the accuracy is worse than the protection level but still falls within the alert limit requirements.

If the plotted point falls within the hazardous misleading information (HMI) section, the integrity monitor fails to do its job and the pilot may be exposed to a hazardous situation.

Figure 7-4: 2D Histogram of Horizontal Error and HPL for Dual Frequency Case #3 with RALT Aiding (σ = 1.0m) (result ID 70)

Table 7-5: Feasibility Study - Single-Frequency GPS Full Availability Results Vertical Constellatio Lateral NSE Horizontal Vertical NSE ID RALT n SV Accuracy Protection Protection Accuracy Number 95% Level Level 95% 1 24 00.00% 00.00% 00.00% 00.00% 2 RALT Off 24+3 (27) 00.00% 00.00% 00.00% 00.00% 3 24+7 (31) 00.00% 00.00% 00.00% 00.00% 4 RALT On 24 00.00% 00.00% 00.00% 00.00% 5 σ = 12.1 24+3 (27) 00.00% 00.00% 00.00% 00.00% 6 meters20 24+7 (31) 00.00% 00.00% 00.00% 00.00% 7 RALT On 24 00.00% 00.00% 00.00% 00.00% 8 σ = .2 24+3 (27) 00.00% 00.00% 00.00% 00.00% 9 meters 24+7 (31) 00.00% 00.00% 00.00% 00.00% 10 RALT On 24 00.00% 100.00% 00.00% 100.00% 11 σ = 1.0 24+3 (27) 00.00% 100.00% 00.00% 100.00% 12 meters 24+7 (31) 00.00% 100.00% 00.00% 100.00% 13 RALT On 24 00.00% 100.00% 00.00% 100.00% 14 σ = 0. 24+3 (27) 00.00% 100.00% 00.00% 100.00% 15 meters 24+7 (31) 00.00% 100.00% 00.00% 100.00% Highlighted cells denote results that meet the GLS D requirements at least 99% of the time.

Table 7-6: Feasibility Study - WAAS Full Availability Results Vertical Constellatio Lateral NSE Horizontal Vertical NSE ID RALT n SV Accuracy Protection Protection Accuracy Number 95% Level Level 95% 16 24 90.90% 00.00% 98.26% 00.00% 17 RALT Off 24+3 (27) 98.26% 00.00% 100.00% 00.00% 18 24+7 (31) 98.89% 00.00% 100.00% 00.00% 19 RALT On 24 92.29% 00.00% 98.26% 00.00% 20 σ = 12.1 24+3 (27) 98.40% 00.00% 100.00% 00.00% 21 meters 24+7 (31) 99.10% 00.00% 100.00% 00.00% 22 RALT On 24 95.35% 00.00% 100.00% 00.00% 23 σ = .2 24+3 (27) 98.96% 00.00% 100.00% 00.00% 24 meters 24+7 (31) 99.86% 00.00% 100.00% 00.00% 25 RALT On 24 97.22% 100.00% 100.00% 100.00% 26 σ = 1.0 24+3 (27) 100.00% 100.00% 100.00% 100.00% 27 meters 24+7 (31) 100.00% 100.00% 100.00% 100.00% 28 RALT On 24 97.22% 100.00% 100.00% 100.00% 29 σ = 0. 24+3 (27) 100.00% 100.00% 100.00% 100.00% 30 meters 24+7 (31) 100.00% 100.00% 100.00% 100.00% Highlighted cells denote results that meet the GLS D requirements at least 99% of the time.

20 Sigma value represents the standard deviation of the error in the terrain elevation database 85

Table 7-7: Feasibility Study - Dual-Frequency GPS Case 1 Full Availability Results Vertical Constellatio Lateral NSE Horizontal Vertical NSE ID RALT n SV Accuracy Protection Protection Accuracy Number 95% Level Level 95% 31 24 55.14% 00.00% 02.85% 00.00% 32 RALT Off 24+3 (27) 76.25% 00.00% 12.15% 00.00% 33 24+7 (31) 83.06% 00.00% 15.76% 00.00% 34 RALT On 24 58.26% 00.00% 02.78% 00.00% 35 σ = 12.1 24+3 (27) 77.92% 00.00% 11.62% 00.00% 36 meters 24+7 (31) 83.96% 00.00% 16.94% 00.00% 37 RALT On 24 62.08% 00.00% 03.47% 00.00% 38 σ = .2 24+3 (27) 86.04% 00.00% 14.17% 00.00% 39 meters 24+7 (31) 87.36% 00.00% 24.31% 00.00% 40 RALT On 24 71.46% 100.00% 06.39% 100.00% 41 σ = 1.0 24+3 (27) 94.86% 100.00% 28.96% 100.00% 42 meters 24+7 (31) 94.24% 100.00% 40.28% 100.00% 43 RALT On 24 72.50% 100.00% 06.53% 100.00% 44 σ = 0. 24+3 (27) 95.21% 100.00% 29.44% 100.00% 45 meters 24+7 (31) 95.00% 100.00% 41.18% 100.00% Highlighted cells denote results that meet the GLS D requirements at least 99% of the time.

Table 7-8: Feasibility Study - Dual-Frequency GPS Case 2 Full Availability Results Vertical Constellatio Lateral NSE Horizontal Vertical NSE ID RALT n SV Accuracy Protection Protection Accuracy Number 95% Level Level 95% 46 24 100.00% 04.58% 40.35% 00.00% 47 RALT Off 24+3 (27) 100.00% 12.01% 74.31% 00.00% 48 24+7 (31) 100.00% 20.00% 85.28% 00.00% 49 RALT On 24 100.00% 05.42% 41.39% 00.00% 50 σ = 12.1 24+3 (27) 100.00% 14.10% 75.49% 00.00% 51 meters 24+7 (31) 100.00% 21.39% 86.11% 00.00% 52 RALT On 24 100.00% 08.26% 49.72% 00.00% 53 σ = .2 24+3 (27) 100.00% 15.69% 82.64% 00.00% 54 meters 24+7 (31) 100.00% 30.28% 89.24% 00.00% 55 RALT On 24 100.00% 100.00% 78.75% 100.00% 56 σ = 1.0 24+3 (27) 100.00% 100.00% 91.94% 100.00% 57 meters 24+7 (31) 100.00% 100.00% 95.07% 100.00% 58 RALT On 24 100.00% 100.00% 82.41% 100.00% 59 σ = 0. 24+3 (27) 100.00% 100.00% 92.71% 100.00% 60 meters 24+7 (31) 100.00% 100.00% 95.28% 100.00% Highlighted cells denote results that meet the GLS D requirements at least 99% of the time.

Table 7-9: Feasibility Study - Dual-Frequency GPS Case 3 Full Availability Results Vertical Constellatio Lateral NSE Horizontal Vertical NSE ID RALT n SV Accuracy Protection Protection Accuracy Number 95% Level Level 95% 61 24 100.00% 94.65% 96.60% 12.15% 62 RALT Off 24+3 (27) 100.00% 99.38% 100.00% 24.24% 63 24+7 (31) 100.00% 98.61% 100.00% 45.76% 64 RALT On 24 100.00% 95.42% 98.06% 15.83% 65 σ = 12.1 24+3 (27) 100.00% 99.38% 100.00% 24.44% 66 meters 24+7 (31) 100.00% 98.61% 100.00% 46.18% 67 RALT On 24 100.00% 95.42% 98.06% 15.83% 68 σ = .2 24+3 (27) 100.00% 99.38% 100.00% 31.74% 69 meters 24+7 (31) 100.00% 98.61% 100.00% 55.14% 70 RALT On 24 100.00% 100.00% 100.00% 100.00% 71 σ = 1.0 24+3 (27) 100.00% 100.00% 100.00% 100.00% 72 meters 24+7 (31) 100.00% 100.00% 100.00% 100.00% 73 RALT On 24 100.00% 100.00% 100.00% 100.00% 74 σ = 0. 24+3 (27) 100.00% 100.00% 100.00% 100.00% 75 meters 24+7 (31) 100.00% 100.00% 100.00% 100.00% Highlighted cells denote results that meet the GLS D requirements at least 99% of the time.

Table 7-10: Feasibility Study - Dual-Frequency GPS Case 4 Full Availability Results Vertical Constellatio Lateral NSE Horizontal Vertical NSE ID RALT n SV Accuracy Protection Protection Accuracy Number 95% Level Level 95% 76 24 100.00% 98.68% 98.54% 50.07% 77 RALT Off 24+3 (27) 100.00% 100.00% 100.00% 67.08% 78 24+7 (31) 100.00% 100.00% 100.00% 84.44% 79 RALT On 24 100.00% 98.68% 98.75% 50.00% 80 σ = 12.1 24+3 (27) 100.00% 100.00% 100.00% 67.15% 81 meters 24+7 (31) 100.00% 100.00% 100.00% 84.58% 82 RALT On 24 100.00% 98.82% 99.65% 61.74% 83 σ = .2 24+3 (27) 100.00% 100.00% 100.00% 67.15% 84 meters 24+7 (31) 100.00% 100.00% 100.00% 89.79% 85 RALT On 24 100.00% 100.00% 100.00% 100.00% 86 σ = 1.0 24+3 (27) 100.00% 100.00% 100.00% 100.00% 87 meters 24+7 (31) 100.00% 100.00% 100.00% 100.00% 88 RALT On 24 100.00% 100.00% 100.00% 100.00% 89 σ = 0. 24+3 (27) 100.00% 100.00% 100.00% 100.00% 90 meters 24+7 (31) 100.00% 100.00% 100.00% 100.00% Highlighted cells denote results that meet the GLS D requirements at least 99% of the time.

Of the six different GNSS configurations (single frequency, WAAS, Dual frequency Cases 1,2, 3, 4), no system alone was able to meet the requirements of a GLS D 87 precision approach and landing without RALT aiding. Apart from the single frequency

GPS case, little improvement is seen to the position solution with RALT aiding until the terrain database error is less than or equal to a standard deviation value of 1.0 meter. This is due to the higher RALT system error standard deviation causing the RALT measurements to be de-weighted compared to the GPS satellites. This shows that the accuracies of the terrain database and the sensor error play a key role in the performance of the RALT-based system. It should also be noted that in all GNSS configurations, the vertical accuracy and VPL are met 100% of the time in the cases where the terrain database error standard deviation is less than 1.0 meters. Given such an accurate measurement, the vertical position is almost solely computed using the RALT measurements rather than the

GNSS pseudoranges.

When the terrain elevation database error was reduced, dual frequency GPS case

#3 and #4 were able to meet all the HPL, VPL, and accuracy requirements of a precision approach and landing at least 99% of the time for all GPS SV constellation numbers. SBAS was able to meet the same requirements at least 99% of the time for GPS constellations with 27 or more active SVs. For the 24 SV case, the SBAS system was able to meet the requirements for over 97% of the simulated day.

For these results, the terrain database vertical error standard deviation must be less than 1.0 meters. While databases do exist with this level of accuracy, they are not always publicly available. Often airports have high accuracy terrain databases of the surrounding area for obstacle clearance evaluation purposes that can be used to create a database of just the approach strip for this application. Assuming the RALT aiding would begin when the 88 aircraft is 500ft above the runway threshold, the total length of the strip would need to be just under two miles long. While some of this area is expected to be flat due to the mentioned FAA obstacle avoidance zones, a large part of this will be outside of airport grounds. Additionally, given the landing characteristics of many RPAs, it is possible to land the aircraft further down the runway to allow for additional radar altimeter measurements on the flat runway surface. This idea is discussed further in chapter 13.2.

This chapter has proposed a RALT-based GNSS augmentation system that would allow the safe, automatic approach and landing of RPAs at airports throughout the geographic areas with suitable SBAS coverage without requiring ground-based equipment.

Given the results provided in this chapter, GNSS augmentation using a downward facing

RALT is a viable option to meet the navigational requirements of fully automatic precision approach and landing. With an accurate terrain database, the current performance of

WAAS, and the expected performance of dual frequency GPS, the accuracy and protection level requirements can be met.

8 FLIGHT TEST ANALYSIS

Following the results of the feasibility study, data recorded on previous flight tests was identified for evaluation of the performance of the RALT Aiding systems using real data. The data used in this study came from two different flight test campaigns the Ohio

University Avionics Engineering Center has been involved in. The first data set was provided by a flight test on a Gulfstream V at the KRNO. The second data set was obtained from a flight test on the Ohio University DC3 flying laboratory at KUNI. These flight tests provided GNSS data and radar altimeter data for use in evaluating the RALT Aiding concept. Note that no SBAS data was used in this evaluation as the SBAS corrections were not available, and dual-frequency GPS was only used for a simple atmospheric error correction. A future flight test is planned to collect all this data.

8.1 Description of Flight Tests

The first of the two flight tests took place on July 21st, 2004 on a Gulfstream V.

During this flight test five manual approaches were flown to runway 16R at the KRNO airport as part of the NASA GVSITE project (a project to investigate the use of synthetic and enhanced vision systems for manned aircraft). The radar altimeter and GPS measurements were recorded once per second. The GPS receiver used on this test was a

L1/L2 dual-frequency GPS receiver which provided observations and ephemeris data. For the analysis of the RALT aiding systems a publicly available terrain database meeting the

DTED1 standards was used. The flight path of the first approach in this flight test is shown in Figure 8-1 overlaid on a satellite image of the KRNO airport. Figure 8-2 presents the same flight path overlaid on an elevation map derived from the DTED1 database. 90

Figure 8-1: Flight Path of KRNO Approach #1 - Satellite Overlay

Figure 8-2: Flight Path of KRNO Approach #1 - Elevation Overlay (DTED1)

The second flight test used in the analysis took place on November 19th, 2011 on the OU DC3 flying laboratory. The aircraft flew one manual approach to runway 25 at

UNI. The radar altimeter on this flight test, a Honeywell HG8505DA01 with a 3dB beamwidth of 17 degrees, recorded data once per second. The GPS receiver, a NovAtel

OEM6 series L1/L2 dual-frequency receiver, recorded observation and ephemeris data once per second. For the analysis or the RALT Aiding system, two terrain maps were used, one publicly available terrain database meeting the DTED1 standard and one publicly available terrain databased generated from LiDAR mapping data. The flight path of the approach in this flight test is shown in Figure 8-3 overlaid on a satellite image of the KUNI airport. Figure 8-4 and Figure 8-5 show the same flight path overlaid on an elevation map derived from the DTED1 database and LiDAR database respectively.

Figure 8-3: Flight Path of KUNI Approach - Satellite Overlay 92

Figure 8-4: Figure 9: Flight Path of KUNI Approach - Elevation Overlay (DTED1)

Figure 8-5: Flight Path of KUNI Approach - Elevation Overlay (LiDAR)

These two flight tests were chosen for analysis for multiple reasons. First, the flight tests recorded the required data for input into the RALT Aiding system. Secondly, they provided a diverse set of approach paths for performance comparison. As can be seen in

Figure 8-1, the KRNO approach path is above the city of Reno, Nevada and contains a large number of manmade structures which will be measured by the radar altimeter. The

KUNI approach path, as seen in Figure 8-3, has fewer manmade structures when compared to KRNO, but includes more foliage.

8.2 Analysis Method

To determine the truth reference position of the aircraft for comparison to the GPS only and RALT Aiding position solution, the recorded dual-frequency GPS observation data was sent to a tural Resources Canada’s Canadian Geodetic urvey (CG ) Canadian

Spatial Reference System (CSRS) Precise Point Positioning (PPS) (ref. [40]) for analysis.

Following the generation of the truth reference data, the recorded GPS observations were processed using the standard LLS method (chapter 4.2.3) to produce a GPS only position solution based on the information available to the GPS receiver at the time of recording.

The same data set was also processed through a RAIM solution-separation method algorithm to determine the position solution’s HPL and VPL. The position solution was then compared to the truth reference position during the approach to determine the lateral and vertical NSE 95% for the GPS only position solution.

Following the computation of the GPS only position solution and protection levels

(PL), the GPS observation and ephemeris data, in addition to the radar altimeter recorded data and the selected terrain elevation database was run through the RALT Aiding 94 algorithm. The algorithm, using the methods described in chapter 6.3.1, produced a position solution, HPL, and VPL during the approach into the airport. The position solution was then compared to the truth reference position during the approach to determine the lateral and vertical NSE 95% for the RALT Aiding position solution.

8.3 Flight Test Results

The following results are presented in terms of 95% lateral and vertical NSE and maximum HPL and VPL for comparison with the GSL F requirements. All results that meet the GSL F requirements are denoted with a green cell.

8.3.1 KRNO Results

The results of the first KRNO approach are presented in Table 8-1. Without RALT

Aiding the GSL F requirements for lateral NSE, vertical NSE, and HPL were met during the approach, but the VPL requirement was not. Following the addition of RALT Aiding, all requirements were met successfully.

Table 8-1: Flight Test Results - KRNO Approach #1 KRNO Lateral Vertical Max HPL Max VPL Approach #1 NSE 95% NSE 95%

GPS Only 2.282 m 3.738 m 13.719 m 23.182 m

RALT Aiding 2.118 m 2.283 m 13.453 m 7.390 m Highlighted cells denote results that meet the GSL F requirements Minimum # SVs Maximum HDOP Maximum VDOP 10 0.9511 1.1388

These results are again presented in 2D histograms plotting error vs protection level. Whereas Figure 8-6 and Figure 8-7 present the results for the un-augmented case, 95

Figure 8-8 and Figure 8-9 show the outcome for the RALT Aiding augmented case. These plots are a visual representation of the current state of the navigation system. During real time operations, the system is unable to compute its true position error, only its current protection level. Based on its calculated protection level (y-axis) it determines if the system is available or not available for use. The error of the system is then plotted on the x-axis to determine if the protection level calculation made the correct determination of the system’s availability, or if the information it provided was MI or hazardously misleading (HMI).

Figure 8-6: 2D Histogram of Horizontal Performance for KRNO Approach #1 - GPS Only

Figure 8-7: 2D Histogram of Vertical Performance for KRNO Approach #1 - GPS Only

Figure 8-8: 2D Histogram of Horizontal Performance for KRNO Approach #1 - RALT Aiding

Figure 8-9: 2D Histogram of Vertical Performance for KRNO Approach #1 - RALT Aiding

The results of the remaining four KRNO approaches are presented in Table 8-2 through Table 8-5.

Table 8-2: Flight Test Results - KRNO Approach #2 KRNO Lateral Vertical Max HPL Max VPL Approach #2 NSE 95% NSE 95%

GPS Only 2.907 m 4.643 m 73.870 m 35.819 m

RALT Aiding 3.852 m 2.516 m 41.662 m 6.613 m Highlighted cells denote results that meet the GSL F requirements Minimum # SVs Maximum HDOP Maximum VDOP 8 1.9864 1.8136

Table 8-3: Flight Test Results - KRNO Approach #3 KRNO Lateral Vertical Max HPL Max VPL Approach #3 NSE 95% NSE 95%

GPS Only 3.049 m 3.878 m 21.573 m 37.412 m

RALT Aiding 2.955 m 1.589 m 20.296 m 7.420 m Highlighted cells denote results that meet the GSL F requirements Minimum # SVs Maximum HDOP Maximum VDOP 9 0.9878 1.4861

Table 8-4: Flight Test Results - KRNO Approach #4 KRNO Lateral Vertical Max HPL Max VPL Approach #4 NSE 95% NSE 95%

GPS Only 3.157 m 4.952 m 18.055 m 44.803 m

RALT Aiding 2.949 m 1.661 m 17.973 m 7.269 m Highlighted cells denote results that meet the GSL F requirements Minimum # SVs Maximum HDOP Maximum VDOP 9 1.0134 1.6559

Table 8-5: Flight Test Results - KRNO Approach #5 KRNO Lateral Vertical Max HPL Max VPL Approach #5 NSE 95% NSE 95%

GPS Only 3.777 m 5.416 m 16.985 m 38.242 m

RALT Aiding 3.522 m 2.103 m 15.433 m 6.790 m Highlighted cells denote results that meet the GSL F requirements Minimum # SVs Maximum HDOP Maximum VDOP 9 1.0473 1.7684

As seen in the above tables, of the five approaches flown into KRNO for this test, none of them met the GSL F navigation requirements before RALT Aiding. Following the addition of RALT aiding, two of the five approaches met he GSL F navigation 99 requirements. All five approaches saw improvements to their vertical NSE, horizontal

NSE, HPL, and VPL except for approach #2 where a slight degradation to the lateral NSE was seen. Even with this degradation, the lateral NSE requirements were still met during the approach.

8.3.2 KUNI Results

Table 8-6 presents the results for the single KUNI approach. In addition to using the DTED 1 terrain database for the KUNI approach, a second database, provided by

LiDAR mapping, was used as a terrain reference. This was done as the DTED1 terrain database did not provided the required performance to provide an improvement to the GPS position. This is explored further in chapter 10.

Table 8-6: Flight Test Results - KUNI Approach KUNI Lateral Vertical Max HPL Max VPL Approach NSE 95% NSE 95% GPS 2.617 m 2.476 m 111.156 m 255.283 m Only RALT Aiding 2.713 m 8.649 m - - DTED1 RALT Aiding 2.322 m 2.332 m 34.789 m 6.271 m LiDAR Highlighted cells denote results that meet the GSL F requirements Minimum # SVs Maximum HDOP Maximum VDOP 5 2.0923 3.9970

For the KUNI approach using GPS and RAIM only, the GSL F navigational requirements were met for lateral and vertical NSE only. Following the addition of RALT

Aiding using the LiDAR generated map, improvements to all four navigation requirements 100 could be observed, but the HPL requirement was still not met. Due to the large errors in the DTED1 database when compared to the true terrain, the addition of RALT Aiding while using the DTED1 database caused the lateral and vertical NSE to become worse when compared to GPS alone.

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9 SENSITIVITY AND ERROR ANALYSIS

Based on the observations in the flight test results, it was determined that a sensitivity study was needed to identify systematic errors. Systematic errors are factors that can degrade the performance of particularly the radar altimeter derived range measurement for use in RALT Aiding. Understanding and characterizing the systematic errors is vital to ensure RALT Aiding meets its intended performance requirements, including the system integrity. Figure 9-1 depicts selected systematic errors and off-nominal conditions and where they can impact the RALT Aiding architecture. More details on the shown errors/faults will be discussed in the next sections.

Figure 9-1: RALT Aiding Architecture with Off Nominal Conditions

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9.1 Systematic Errors

Two sets of systematic have been examined in this study, one set for the terrain elevation database and one set for the radar altimeter unit. The systematic errors that can impact the terrain elevation database are: (i) interpolation errors, (ii) lateral position errors, and (iii) age of data. Additionally, the systematic errors that can impact the radar altimeter are: (i) aircraft attitude, (ii) foliage, and (iii) undetected faults.

The work discussed in chapter 7 and chapter 8 show that the accuracy of the terrain elevation database is a large factor in RALT Aiding’s ability to augment the GP positions solution in a meaningful way. The feasibility study showed that RALT Aiding using a terrain elevation database with a vertical standard deviation 휎푡푒푟푟푎푖푛 of 1.0 meters is low enough for RALT Aiding to meet the GLS F navigational requirements given an ARINC

707 compliant radar altimeter. While previous analyses in this thesis only used 휎푡푒푟푟푎푖푛 to represent the error statistics of the terrain elevation database, the full error statistics must include an error statistics introduced tom for the terrain database and each relevant systematic error. The new error statistics for the terrain elevation database and the

21 associated systematic error is 휎퐷퐸푀 and is a combination of the interpolation error

2 ퟐ 2 (휎푖푛푡푒푟푝), lateral offset induced error (휎 푙푎푡.표푓푓.), the attitude induced error (σ푎푡푡푖푡푢푑푒), and

2 22 the vertical error in the terrain elevation database (휎푡푒푟푟푎푖푛 ). These error standard deviations are then combined using the root sum squared (RSS) method to form the overall database vertical error:

21 Previous study [4] referred to this value as 휎푡표푡푎푙 22 Previous study [4] referred to this values as 휎푣푒푟푡.푒푟푟표푟 103

2 2 2 2 휎퐷퐸푀 = √휎푖푛푡푒푟푝 + 휎 푙푎푡.표푓푓. + σ푎푡푡푖푡푢푑푒 + 휎푡푒푟푟푎푖푛 = 1.0푚 (45)

Following this change, equation (19) is updated to:

2 2 2 σ푅퐴퐿푇 = √σ푠푡푎푡푖푐 + σ푎푙푡 + σ퐷퐸푀 (46)

If each of the sub-error statistics in equation (45) were given equal allocation to

RSS to 1.0 meters or less, each error component can have a maximum standard deviation of 0.5 meters. Although, as will be discussed further, it may not be accurate to represent each error by a standard deviation.

9.1.1 Interpolation Error Analysis

Given data storage limitations, the terrain elevation database cannot store a continuous map of the terrain, but instead stores elevation values at discrete latitudes and longitudes given a predefined post-spacing. To determine the elevation of the terrain between the recorded points, a 2D interpolation algorithm is used to obtain a best estimate of the terrain elevation at that location. If the terrain is highly variable (i.e. large undulations), or the post spacing of the database is too large, the interpolated height of the terrain in the database may not accurately represent the true height of the terrain at the referenced location (latitude, longitude). This height difference introduces an error between the estimated range and true range used in the LLS algorithm, and will, in turn, introduce an error into the RALT Aiding augmentation. The interpolation error mechanism is illustrated in Figure 9-2.

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RALT easured Height

stimated Range

Database Interpolation rror

Figure 9-2: Interpolation Error

To determine the impact of the interpolation error on the accuracy of the terrain elevation estimate extracted from the terrain elevation database, LiDAR maps at two different airport locations (KUNI airport near Athens, Ohio and KRNO airport near Reno,

NV) were up-sampled to a post spacing of 0.1 meters and used as a truth reference for the terrain. Since the two original terrain elevation databases did not have consistent spacing between each point, the created truth reference map has been generated with a grid of points

0.1 meters apart. A section under the approach path in front of each test runway was then isolated from the terrain database and used for the test. The size of this section is 3,000 meters along track by 130 meters cross track. This test zone size was selected as it is the area “sensed” by a radar altimeter with a 45° beamwidth antenna starting a height of 500ft

AGL above the touchdown point on a 3° glideslope. The tests conducted at KUNI were performed on the approach path to runway 25 and the tests conducted at KRNO were performed on the approach path to runway 16R. Figure 9-3 through Figure 9-6 present the 105 satellite image and the LiDAR generated terrain elevation truth map for KUNI and KRNO, respectively.

Figure 9-3: KUNI Approach Test Area – Satellite Overlay

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Figure 9-4: KUNI Approach Test Area – LiDAR Elevation Overlay

Figure 9-5: KRNO Approach Test Area – Satellite Overlay

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Figure 9-6: KRNO Approach Test Area – LiDAR Elevation Overlay

Next, the isolated approach test areas were down sampled from the original 0.1 meter spacing to post-spacings ranging from 0.5 to 30 meters to represent various terrain elevation onboard database storage options. These down-sampled terrain elevation databases were then interpolated back to the original post spacing of 0.1 meters. Given the original terrain elevation databases did not have even post spacing, the down sampled 2.0 meters post spacing terrain elevation database is significantly different than the original terrain elevation database. The difference between the original truth map and the interpolated maps is taken to be the interpolation error at a given post spacing. The results of this test are presented in Table 9-1 and Table 9-2.

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Table 9-1: KUNI Interpolation Error Results Post Spacing Mean Error Max Error Std. Of Error 95% Error 30.0 m 0.580 m 20.12 m 1.460 m 2.538 m 20.0 m 0.518 m 21.39 m 1.411 m 2.697 m 10.0 m 0.364 m 22.29 m 1.213 m 2.055 m 5.0 m 0.247 m 21.12 m 0.983 m 1.353 m 1.0 m 0.056 m 16.98 m 0.349 m 0.188 m 0.5 m 0.020 m 13.56 m 0.156 m 0.046 m

Table 9-2: KRNO Interpolation Error Results Post Spacing Mean Error Max Error Std. Of Error 95% Error 30.0 m 0.733 m 15.42 m 1.566 m 3.478 m 20.0 m 0.577 m 13.82 m 1.356 m 2.869 m 10.0 m 0.290 m 11.27 m 0.719 m 1.442 m 5.0 m 0.094 m 6.06 m 0.240 m 0.469 m 1.0 m 0.023 m 1.89 m 0.067 m 0.125 m 0.5 m 0.022 m 2.19 m 0.066 m 0.115m

Given the results of this test, the optimal post-spacing for terrain elevation databases for use with RALT Aiding is believed to be 1.0 meters if a constant spacing database is used. This was selected as in the case of the KUNI test, the test with the highest post spacing that still met the 0.5-meter standard deviation was 1.0 meters. It should be noted that the error between the truth database and tested database is dependent on the location of the aircraft relative to a recorded elevation point as well as the terrain on the approach path. In most cases test cases, the max error seen was due to artifacts or anomalies in the data. These could be from tall manmade structures such as telephone poles or natural objects such as bare trees that are present in the original database but are excluded from the interpolated version.

Given a maximum beamwidth of 45° and the operation range starting at 500ft AGL, the minimum width of the terrain elevation database for use in RALT Aiding is 130 meters 109 and the minimum length is 3,000 meters. With a post-spacing of 1.0 meters, this will result in a minimum of 390,000 recorded elevation points in the onboard terrain database.

While not explored in this study, it is possible to generate a terrain elevation database with non-constant spacing between recorded elevation points. This spacing method would allow for additional points to be included for areas of highly variable terrain where large interpolation errors are expected, and fewer point to be included in areas of flat, slowly changing terrain. This method allows for database space to be saved where additional points are not needed, but still allows for accurate terrain data where required.

Additionally, future implementations of RALT Aiding may consider using not one but a time history of radar altimeter measurements and use batch (e.g. batch LS) or sequential estimators (e.g. Kalman filters) rather than a snapshot position solution such as the LLS and WLS. Multiple range measurements taken over a short period will help reduce and errors introduced through the interpolation process. Also, methods such as weighting the radar altimeter derived measurement in the overall position calculation based on the aircraft’s distance from the touchdown point and distance from the nearest measured terrain post can improve the overall measurement accuracy. This would mean that the error statistics are no longer for the entire terrain database along the approach, but changes based on the location of the aircraft.

9.1.2 Lateral Offset Error Analysis

To obtain a terrain elevation value from the terrain database via a database lookup, i.e. ℎ푑푒푚 = 푓(푙푎푡, 푙표푛) an estimated aircraft position is required before the RALT Aiding augmentation can be implemented. Any lateral error in this position estimate may introduce 110

푅퐴퐿푇 a database lookup error resulting in an error in the vertical range estimate, 휌푚푒푎푠 as the value provided by the terrain elevation database will not be providing elevation data for the terrain directly below the aircraft but at a point offset from the true position. This error mechanism is illustrated in Figure 9-6.

True Aircraft Position stimated Aircraft Position

RALT easurement stimated Range

Lateral Offset Induced rror

Figure 9-7: Lateral Offset Induced Error

To determine the vertical error introduced by an error of the horizontal position input, two zero-mean, normally distributed datasets with a standard deviation of 8.17723 meters were generated to represent the cross track and along track error of the unaugmented position estimate. This along-track and cross-track error was then applied to the position estimate when accessing information from the terrain elevation database. The difference between the elevation value at the true position and the elevation found at the

23 8.117 meters represents one standard deviation when using the LPV Horizontal Alert Limit of 40.0 meters as a 99.9999% containment value 111 erroneous position is considered as the lateral offset induced vertical error. This test was performed at four different locations along the approach path of the runway, at 2330 meters

(400 ft AGL), 1735 meters (300 ft AGL), 1150 meters (200 ft AGL), and 580 meters (100 ft AGL) from the touchdown point. Spacing the test zones equally along the approach flight path allows for an error estimate to be determined based on the aircraft’s distance from the touchdown point. The spacing of the test zones are depicted in Figure 9-8.

Figure 9-8: Lateral Offset Test Zone Spacing

Figure 9-9 presents a histogram of the vertical error due to a lateral offset error for the four test zones along the KUNI runway 25 approach path.

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Figure 9-9: Lateral Error Induced Vertical Error - KUNI Approach

Table 9-3 presents the numerical results of the KUNI approach analysis.

Table 9-3: Lateral Offset Induced Vertical Error - KUNI Approach Distance from Touchdown Mean Max Std. Of 95% Point Error Error Error Error 2330 m 1.737 m 9.277 m 2.377 m 6.093 m 1735 m 0.860 m 1.320 m 1.320 m 2.813 m 1150 m 0.086 m 1.089 m 0.123 m 0.325 m 580 m 0.182 m 1.067 m 0.227 m 0.454 m

The results for the test conducted on the KRNO approach path data are shown in

Figure 9-10 and Table 9-4.

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Figure 9-10: Lateral Offset Induced Vertical Error - KRNO Approach

Table 9-4: Lateral Offset Induced Vertical Error - KRNO Approach Distance from Touchdown Mean Max Std. Of 95% Point Error Error Error Error 2330 m 0.909 m 8.490 m 1.387 m 3.674 m 1735 m 0.409 m 7.444 m 0.689 m 1.396 m 1150 m 0.243 m 1.374 m 0.096 m 0.359 m 580 m 0.066 m 0.493 m 0.075 m 0.216 m

As expected, the further away the aircraft is from the touchdown point on the runway, the more significant the impact of the lateral offset on the vertical error. This can be attributed to the fact that the terrain closer to the runway tends to be flatter (i.e. less terrain variations) and more consistent when compared to the terrain further away from the runway which may be outside of the airport grounds. The error introduced by the highly varying terrain signature outside of the airport grounds can be clearly observed in histogram of the vertical error at 2330 meters from the touchdown point in both the KUNI and KRNO case. The histograms in these two test cases both have tails that extend far away 114 from the center of the histogram due to a large undulation in the terrain around the test zone. In the KUNI test case, there exists a large variation in the terrain that results in a bi- modal error distribution with one mode centered around 0 meters and another centered around -6 meters of error. This large error is due to a large difference between the terrain below the aircraft and the terrain at the location being pulled from the terrain database.

Similar to the method discussed in chapter 9.1.1, it is expected that these undesirable tails can be reduced or removed all together by including multiple range measurement over time in the position estimator rather than a single measurement. This method would mitigate the error due to the cross-track and along-track error at a given point by extending the number of measurements over a longer section of the terrain if the errors are varying and uncorrelated. It should be noted that if the errors are correlated (such as a hill to the side of the aircraft), a bias could be added to the position solution that would be sustained in the estimate for some time.

The previously discussed results assume a cross-track and along-track standard deviation of 8.117 meters. This standard deviation relates to the HAL of LPV enabled by

WAAS. While the HAL is a guaranteed containment of the user position during an LPV approach, the true horizontal accuracy of WAAS is much better than the listed specification. According to [21], the worst case 95% horizontal error when LPV was available between January and March of 2019 was 1.374 meters. This 95% error relates to a stand deviation of 0.7013 meters. Using this standard deviation as an input for the cross track and along track errors, a more realistic estimate of the lateral offset induced vertical 115 error can be calculated. The results of this test case are presented in Figure 9-11 and Table

9-5 for UNI, and Figure 9-12 and Table 9-6 for KRNO.

Figure 9-11: Lateral Offset Error Induced Vertical Error - KUNI Approach with Reduced Lateral Error

Table 9-5: Lateral Offset Error Induced Vertical Error - KUNI Approach Results with Reduced Lateral Error Distance from Touchdown Mean Max Std. Of 95% Point Error Error Error Error 2330 m 0.058 m 5.537 m 0.176 m 0.139 m 1735 m 0.043 m 0.272 m 0.055 m 0.111 m 1150 m 0.005 m 0.037 m 0.006 m 0.013 m 580 m 0.015 m 0.086 m 0.019 m 0.038 m

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Figure 9-12: Lateral Offset Error Induced Vertical Error - KRNO Approach with Reduced Lateral Error

Table 9-6: Lateral Offset Error Induced Vertical Error - KRNO Approach Results with Reduced Lateral Error Distance from Touchdown Mean Max Std. Of 95% Point Error Error Error Error 2330 m 0.283 m 2.202 m 0.352 m 0.708 m 1735 m 0.070 m 0.998 m 0.120 m 0.340 m 1150 m 0.050 m 0.210 m 0.034 m 0.113 m 580 m 0.004 m 0.029 m 0.005 m 0.010 m

With the reduced lateral error based on the measured performance of WAAS, the lateral offset induced vertical error is greatly reduced compared to the error introduced when using the WAAS performance standards.

The error statistics calculated in the section are independent of the flight and are a function of the terrain database. These error statistics will be different for every airport based on the terrain below the approach path. 117

9.1.3 Attitude Error Analysis

The attitude of the aircraft, specifically its pitch and roll, can negatively impact the

푅퐴퐿푇 accuracy of 휌푚푒푎푠. The error due to the aircraft attitude is introduced at two different points in the process of calculating the GPS antenna height.

The first place is during the lever arm correction (i.e. addition of the vertical offset) between the radar altimeter antenna and the GPS antenna. The vertical distance between the radar altimeter antenna and the GPS antenna must be accounted for when calculating the height of the GPS antenna relative to the WGS 84 ellipsoid from the radar altimeter measurement and terrain database. If the aircraft has a large pitch or roll, the vertical distance between the two antennae may change. This vertical change is illustrated in Figure

9-13.

GP Antenna Height 0° Pitch RALT Antenna Height 20° Pitch RALT Antenna Height

Pitch Induced rror

Figure 9-13: Pitch Induced Height Error between GPS and Radar Altimeter Antennas

ince it is not e pe cted that both the aircraft’s pitch and roll to e c eed 10° when below 500ft AGL, the worst-case vertical change between the antennas can be calculated by rotating their locations around the aircraft’s center of gravity. Given the locations of the antennas on the Ohio University DC-3, the worst-case vertical difference relative to level flight is when the aircraft is at a pitch of 10° and no roll. A pitch of 10° introduces a vertical 118 difference between the antennas of 0.15 meters (0.49ft) when compared to level flight.

Given the expected locations of the antennas on a Medium Altitude-Long Endurance

(MALE) RPA, the worst-case vertical difference relative to level flight is when the aircraft is at a roll of 10° and a pitch of 10°. A roll and pitch if of 10° introduces a vertical difference between the antennas of 0.12 meters (0.393ft) when compared to level flight.

This error can be corrected by introducing a lever arm calculation based on the pitch and roll of the aircraft, but given that the error in both aircraft’s worse case scenarios is less than the error specification for the radar altimeter discussed in chapter 4.6 it is not expected that any correction is needed.

The second way an error can be introduced due to the aircraft’s attitude is through the change in the area that the radar altimeter “senses”. The effect of this change in observed area is depicted in Figure 9-14. If the radar altimeter would only measure a small point in the pointing direction, similar to a laser altimeter, the point of measurement would no longer be directly below the aircraft. Using the max operational height of 500ft AGL for RALT Aiding and the maximum pitch and roll of 10°, the measurement point will be

26.8 meters in front of the aircraft and 26.8 meters to the right of the aircraft. This change in measurement location will introduce a vertical error of 4.72 meters on level terrain and could introduce a larger error on non-level terrain. It should be noted that this calculation does not consider the difference in the location of the transmit and receive antennas, but this would not significantly change the results.

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RALT easurement True Range RALT AGL stimate Pitch Induced rror

Figure 9-14: Pitch Induced Radar Altimeter Measurement Error

While the error calculation above is true if the radar altimeter only measured a small point in the pointing direction, the antennas expected to be used in this application will have a beamwidth between 20° and 45°. Since the maximum pitch and roll is less than the minimum beamwidth, even during worst case pitch and roll, the antennas will still be illuminating the ground directly below the aircraft, and it is not expected that a large change in the AGL measurement will be seen unless a large variation in the terrain is present. An example of the difference between the scan area of level flight and flight with a slight roll is presenting in Figure 9-15. It should also be noted that the specifications presented in chapter 4.6 state that the radar altimeter accuracy requirements must be met with a maximum pitch of 20° and a maximum bank of 40°.

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10° Roll can Area

0° Roll can Area

Figure 9-15: Radar Altimeter Antenna Beamwidth Scan Area

9.1.4 Other Error Conditions

In addition to the previously discussed conditions, additional error conditions exist that can negatively impact the performance the radar altimeter derived measurement for use in RALT Aiding.

9.1.4.1 Foliage

All ranging altimeters see foliage in varying levels of detail. Since this architecture uses a radar altimeter with a frequency range of 4.235 to 4.364 GHz, most range measurements will be of the ground or structure below the aircraft, and foliage will not be observed. Since the terrain database is expected to be a bare earth model (with buildings and other large structures) any foliage returns by the radar altimeter may introduce an error in the radar altimeter measurement. 121

9.1.4.2 Undetected Faults

Additional undetected faults in the radar altimeter unit can introduce unexpected error conditions. These faults include instrument failures, incorrect calibration and incorrect installation.

9.1.4.3 Age of Data

Since the terrain database is recorded at a set point in time, any changes to the terrain after the date of recording can impact the accuracy of the database. The portion of the terrain in the terrain database nearest the touchdown point on the runway will be contained inside the airport grounds. It is not expected that this terrain will change often, and any changes to the terrain will be well documented. Given that the operational along track range of RALT Aiding is around 3,000 meters a large portion of the terrain database will fall outside out the airport grounds. Changes to the terrain outside of the airport grounds may happen more frequently and without documentation.

9.2 Terrain Database Comparison

As seen in the results of the KUNI approach, the terrain database plays a large role in the performance of the RALT Aiding system. If the database does not provide enough information or accurate information about the terrain, the RALT Aiding system can degrade the performance of the GPS position solution. Figure 9-16 shows the height of the terrain below the aircraft during the KUNI approach as recorded in the DTED1 and LiDAR terrain databases.

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Figure 9-16: Terrain Height below Aircraft during KUNI Approach

At certain points during the approach, the difference in height between the DTED1 terrain database and the LiDAR terrain database varies by over 10 meters. While this figure only compares the differences between the two databases and not their absolute accuracy, the large discrepancies in the two databases show that a further investigation for the optimal database for this application is required.

Using the truth reference of the aircraft and the RALT measurements, a synthesized terrain as seen by the aircraft can be calculated and compared to the terrain databases.

Figure 9-17 compares the differences between the measured terrain and the two terrain databases.

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Figure 9-17: Difference between Terrain Databases and Synthesized Terrain

Even though the difference between the synthesized terrain and the LiDAR database shows more noise during the first 10 seconds of the approach, overall the LiDAR database more closely matches what the aircraft was measuring than the DTED1 database.

It is believed that the noise at the beginning of the approach using the LiDAR database can be attributed to foliage recorded in the LiDAR database that is not observed by the RALT

(due to the frequency at which it makes its measurements). Table 9-7 presents the results in terms of max difference and standard deviation of the difference.

Table 9-7: Difference between Terrain Databases and Synthesized Terrain Max Difference STD. of Difference DTED1 10.092 m 3.492 m LiDAR 7.953 m 1.346 m

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Due to the large spacing of the recorded points in the DTED1 database (3 arc seconds by 3 arc seconds), undulations in the terrain between the measured points are not accurately represented in the database causing a large error in the RALT range estimation.

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10 SPOT ALGORITHM IMPROVEMENT CONCEPT

Currently, the RALT Aiding algorithm chooses the elevation from the terrain elevation database using the plumb bob method, where only a single value is selected from directly below the aircraft. While this method is simple to implement, as discussed in chapter 9.1.3, the type of radar altimeter and beamwidth of the antenna may result in a radar altimeter height AGL measurement corresponding to a range measurement to a location that is not directly below the aircraft. To account for this, a compensation method was proposed in [23] referred to as the spot algorithm concept. In this algorithm, instead of choosing the plumb bob elevation of the terrain below the aircraft, a “spot” is defined that corresponds to the expected area illuminated by the radar altimeter. This “spot” is then accessed from the terrain elevation database and the shortest range is selected from this area. The analysis in this section builds on that method by examining the plumb bob range, shortest range, longest range, and mean range in the illuminated zone. Figure 10-1 shows the spot algorithm concept of operation.

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Figure 10-1: Spot Algorithm Concept of Operation

To evaluate the performance gained by the addition of the spot algorithm, the terrain information provided by the spot algorithm is compared to a synthesized version of the terrain created by taking the true altitude of the aircraft minus the radar altimeter measurement. The synthesized terrain represents the terrain that the radar altimeter sees.

While the difference between the spot algorithm’s terrain and the synthesized terrain does not directly provide information about the accuracy of spot algorithm’s terrain information, it provides information about the difference between the terrain database and the terrain measured by the radar altimeter. This difference, Δ푡푒푟푟푎푖푛, is calculated by:

Δ푡푒푟푟푎푖푛 = ℎ푠푝표푡 − ℎ푠푦푡ℎ푒푠푖푧푒푑 (47)

Where the synthesized terrain, ℎ푠푦푡ℎ푒푠푖푧푒푑 , is calculated by:

ℎ푠푦푡ℎ푒푠푖푧푒푑 = ℎ푎푔푙 푡푟푢푡ℎ − 푅퐴퐿푇푚푒푎푠푢푟푒푑 (48) 127

To test this algorithm, two different sets of flight test data were used. One data set included an approach to KUNI runway 25 and the other an approach to KRNO runway

16R. Both flight tests had an onboard radar altimeter that recorded data at a rate of 1 Hz.

The spot algorithm was used on both the DTED1 terrain database and the LiDAR generated terrain database.

Figure 10-2, Table 10-1, and Table 10-2 present the terrain difference for the four different spot algorithms during the KUNI approach flight test.

Figure 10-2: KUNI Spot Algorithm Elevation Differences

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Table 10-1: Difference between KUNI DTED1 Spot Algorithm Terrain and Synthesized Terrain Spot Max Std. Of 68% 95% 99.7% Algorithm Difference Difference Difference Difference Difference Method Plumb Bob 11.794 m 3.537 m 3.638 m 9.327 m 11.794 m Mean 11.794 m 3.536 m 3.638 m 9.327 m 11.794 m Elevation Shortest Range 11.554 m 3.639 m 3.543 m 9.236 m 11.554 m Elevation Longest Range 12.037 m 3.446 m 3.228 m 9.418 m 12.037 m Elevation

Table 10-2: Difference between KUNI LiDAR Spot Algorithm Terrain and Synthesized Terrain Spot Max Std. Of 68% 95% 99.7% Algorithm Difference Difference Difference Difference Difference Method Plumb Bob 2.704 m 0.889 m 1.421 m 2.234 m 2.704 m Mean 2.940 m 0.749 m 1.484 m 2.399 m 2.940 m Elevation Shortest Range 7.246 m 1.782 m 2.937 m 6.551 m 7.246 m Elevation Longest Range 12.607 m 3.688 m 1.142 m 11.539 m 12.607 m Elevation

In the case of the KUNI approach, none of the three spot methods had a large positive impact on the performance of the radar altimeter measurement when compared to the plumb bob height. Figure 10-3, Table 10-3, and Table 10-4 show the terrain difference for the four different spot algorithms during the KRNO approach flight test.

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Figure 10-3: KRNO Spot Algorithm Elevation Differences

Table 10-3: Difference between KRNO DTED1 Spot Algorithm Terrain and Synthesized Terrain Spot Max Std. Of 68% 95% 99.7% Algorithm Difference Difference Difference Difference Difference Method Plumb Bob 2.617 m 0.967 m 1.034 m 1.719 m 2.617 m Mean 2.363 m 0.946 m 1.059 m 1.965 m 2.363 m Elevation Shortest Range 3.017 m 1.071 m 1.078 m 2.030 m 3.017 m Elevation Longest Range 2.599 m 0.894 m 1.096 m 1.913 m 2.599 m Elevation

130

Table 10-4: Difference between KRNO LiDAR Spot Algorithm Terrain and Synthesized Terrain Spot Max Std. Of 68% 95% 99.7% Algorithm Difference Difference Difference Difference Difference Method Plumb Bob 9.418 m 1.559 m 1.162 m 2.539 m 9.418 m Mean 3.561 m 0.7933 m 1.270 m 1.967 m 3.561 m Elevation Shortest Range 4.314 m 1.261 m 1.209 m 2.496 m 4.314 m Elevation Longest Range 14.836 m 3.405 m 3.661 m 10.723 m 14.836 m Elevation

imilar to the spot algorithm’s impact during the KUNI approach, not much improvement can be observed for the KRNO data for application of the three spot algorithm methods using the DTED1 database. However, when using the LiDAR terrain elevation map, an improvement over the plumb bob method is observed when compared to the spot algorithm’s mean elevation and shortest-range elevation. This can be attributed to the difference in terrain below the approach path between KUNI and RNO; Whereas

KUNI has very few variations due to man-made obstacles in the approach path, the KRNO approach has the aircraft flying over multiple man-made structures such as buildings and bridges. These man-made structures change the terrain very rapidly and are often seen by the radar altimeter before the aircraft is directly above it. The spot algorithm is able to account for the radar altimeter seeing the building before the aircraft is directly above and can account for it.

Further exploration into the advantages and disadvantages of this method is required. While this method has the ability to improve the vertical range estimate to be 131 used in RALT Aiding, it introduces and additional horizontal error component that needs to be accounted for in the error budgets [23]. Additionally, the type of radar altimeter, FM-

CW or PM, and antenna beamwidth will change the performance of the spot algorithm and must be analyzed given those variables.

11 APPROACH FOR CERTIFICATION

Many of the subsystems used in the RALT Aiding method proposed in this thesis have already been certified and applied to approved procedures. Developing around these certified systems means the integration of their outputs for another approach and landing system is a good starting point for a firm certification basis.

11.1 FAA Standards Development Process

When developing new standards and performance requirements, the FAA and other state aviation regulation organizations gets assistance from technical guidance organizations such as RTCA. These technical guidance organizations, who are made up of government, industry and academic experts, deliver recommendations for equipment through MOPS and MASPS. Using the MOPS and MASPS as a guideline, the FAA publishes Technical Standard Orders (TSO) that manufacturers can follow for developing avionics hardware [41]. The typical MOPS, MASPS, and TSO development process is presented in Figure 11-1.

132

Figure 11-1: Avionics Standards Development Process (derived from [41])

In addition to the A P which “specify characteristics that are useful to designers, installers, manufacturers, service providers and users of systems intended for operational use within a defined airspace” [42] and OP which “provide standards for specific equipment(s) useful to designers, manufacturers, installers and users of the equipment” [43] RTCA produces other documents to help guide regulators and manufactures. These other documents include safety performance requirements (SPR), operational services and environment definitions (OSED), and interoperability requirements (INTEROP). All of these standards and guidance materials are developed through special committees (SC) whose members are experts in that field. For a system such as RALT Aiding to be certified, an existing SC will have to be called or a new SC will need to be formed to develop specific MASPS and MOPS for the architecture.

11.2 Certification of RALT Aiding

Similar to many other recently developed navigation systems such as ABAS,

WAAS, and GBAS, RALT Aiding builds upon many widely adopted and previously certified navigation systems. By using previously certified subsystems in its architecture, many aspects of RALT Aiding are already certifiable, and the main focus for certification 133 will be on the novel aspects, integration of the subsystems, and any gaps between the functions of the certified subsystems and the overall system. The RALT Aiding airborne equipment is presented in Figure 11-2 with the current standards associated with its subsystems and selected standards they may help in certifying new aspects of the system.

Figure 11-2: RALT Aiding Airborne Equipment

To determine which aspects of the RALT Aiding architecture will require additional standards documents, a safety assessment and gap analysis must be completed.

This process will determine where previous standards are acceptable for this new architecture and where a standards SC will need to develop additional standards. To 134 develop a preliminary idea of where standard gaps may exist, the following section provides a brief comparison between the current relevant standards and the RALT Aiding architecture.

11.2.1 Standards Comparison

Currently SBAS is certified for stand-alone en-route navigation, NPAs, and APVs with the most stringent procedure using SBAS being LPV-200. Some of the accepted standards a requirements documents for SBAS are RTCA DO-229E Minimum Operational

Performance Standards for Global Positioning System/Satellite-Based Augmentation

System Airborne Equipment (ref. [11]), ICAO Annex 10 Aeronautical Telecommunications

(ref. [44]), FAA TSO-C145c Airborne Navigation Sensors Using the GPS Augmented by the Satellite-Based Augmentation System (ref. [45]), and FAA TSO-C146e Stand-Alone

Airborne Navigation Equipment Using the GPS System Augmented by the Satellite-Based

Augmentation System (ref. [46]). Additionally the standards and guidance for the aircraft’s

GPS antenna is provided in RTCA DO-301 Minimum Operational Performance Standards for Global Navigation Satellite System (GNSS) Airborne Active Antenna Equipment for the

L1 Frequency Band (ref. [47]), RTCA DO-373 MOPS for GNSS Airborne Active Antenna

Equipment for the L1/E1 and L5/E5a Frequency Bands (ref. [48]), and FAA TSO-C190

Active Airborne Global Navigation Satellite System (GNSS) Antenna (ref. [49]). These documents provide a solid foundation of the certification of SBAS within RALT Aiding, but certain requirements for a RALT Aiding GPS receiver are not defined in these documents. Currently, WAAS is only approved for an APV down to an AGL height of 200 ft, standards will need to be updated showing that WAAS augmented with RALT Aiding 135 can meet the accuracy, integrity, and continuity requirements for a zero-foot decision height PA. On top of that, WAAS systems are designed to provide position and integrity information to the navigation computer. For use in RALT Aiding, the WAAS subsystem will need to provide all raw measurements and SBAS messages to the RALT Aiding processing computer for the RALT Aiding position computation. At the moment, WAAS systems are not designed with this feature. Additionally, there are some WAAS standards that are not relevant to and RALT Aiding implementation such as the aspects related to the on-board human machine interface.

Radar altimeters are certified for autoland flare maneuvers and to support altitude determination during autoland. Some of the standards supporting the certification of radar altimeters are RTCA DO-155 Minimum Performance Standards Airborne Low-Range

Radar Altimeters (ref. [43]), ARINC 707 Radio Altimeter (ref. [24]), and FAA TSO-C87

Airborne Low-Range Radio Altimeter (ref. [50]). These standards were not written with the new intended function of the radar altimeter within the RALT Aiding navigation architecture and must be updated accordingly.

Terrain databases are currently used in TAWS systems and are regulated through

RTCA DO-272C User Requirements for Aerodrome Mapping Information (ref. [31]),

RTCA DO-276B User Requirements for Terrain & Obstacle Data (ref. [32]), and RTCA

DO-291B Minimum Interchange Standards for Terrain, Obstacle and Aerodrome Mapping

Data (ref. [51]). While these documents provide a starting point for the standards development for a terrain database to be used with RALT Aiding, no current standards meet the expected performance requirements for a terrain database for RALT Aiding. New 136 standards must be written to cover all aspects of the terrain database for the airport approach path, including information about the recorded terrain size, post spacing, point accuracy, integrity, and any additional database parameters. Additionally, no standard covers the multiple methods for interpolating data from the terrain database for an augmentation method such as RALT Aiding.

The subsystem with the least pre-existing standards and guidance is the RALT

Aiding processing computer. This subsystem has the essential role of interfacing with all other subsystems, computing the current position estimate, calculating the current integrity, and communicating with the autopilot. While standards do exist discussing software requirements, airborne electronic hardware, and computer resources, RTCA DO-178

Software Considerations in Airborne Systems and Equipment Certification (ref. [52]),

RTCA DO-254 Design Assurance Guidance for Airborne Electronic Hardware (ref. [53]), and RTCA DO-255 Requirements Specification for Avionics Computer Resource (ref.

[54]) respectively, a standard outlining the RALT Aiding method will be needed. This includes items such as the method for position calculation, integrity calculation, and deriving a pseudorange measurement from the radar altimeter range measurement. To assist in this task, technical reports similar to the GEAS Phase I (ref. [15]) and Phase II

(ref. [37]) can be leveraged as a starting point for the continued exploration of GNSS based approach systems.

137

12 SUMMARY AND CONCLUSIONS

The goal of this work is to develop an on-board navigation system capable of RPA precision approach and landing without any ground-based navigational infrastructure at the arrival airport. An on-board GPS augmentation system using both a downward facing radar altimeter and a terrain elevation database was proposed. This system is expected to allow for safe and automatic landing of RPAs throughout the geographic areas where suitable

SBAS coverage exists or when dual-frequency GPS or GNSS is available to aviation users.

A feasibility study has been conducted to determine the expected performance of the proposed architecture. The RALT Aiding feasibility study concluded that:

i. No current unaugmented GNSS systems (single-, dual-frequency, or

WAAS) can meet the GLS F precision approach and landing requirements

without RALT Aiding.

ii. In all GNSS configurations, RALT Aiding with a terrain database error

standard deviation less than 1.0 meters met the GSL F vertical accuracy and

VPL requirements were met during 100% of the day.

iii. RALT Aiding augmentation with dual-frequency GPS (test cases #3 & #4)

with a terrain database error standard deviation less than 1.0 meter met the

GSL F vertical accuracy, horizontal accuracy, VPL, and HPL for at least

99% of the day.

iv. RALT Aiding with SBAS, a terrain database error standard deviation less

than 1.0 meter, and a GPS constellation with 27 or more active SVs met 138

the GSL F vertical accuracy, horizontal accuracy, VPL, and HPL during at

least 99% of the day.

Additionally, a flight test analysis has been conducted to determine the real-world performance of the RALT Aiding augmentation method. Two flight test campaigns, one at

KUNI on the Ohio University DC3 and one at KRNO on a NASA Gulfstream V, were analyzed. The results of this analysis showed that:

i. No approach met the GLS F navigational requirements before RALT

Aiding.

ii. Two of the five KRNO approach met the GLS F vertical NSE, horizontal

NSE, HPL, and VPL following RALT Aiding.

iii. All five KRNO approaches saw improvement to their vertical NSE,

horizontal NSE, HPL, and VPL when using RALT Aiding.

iv. The KUNI approach met the GSL F vertical NSE, horizontal NSE, and VPL

when using RALT Aiding with a LiDAR generated terrain elevation

database.

It should be noted that this test did not use SBAS or the full advantages of dual- frequency GPS. A full implementation of RALT Aiding would take advantage of the increased accuracy and integrity of SBAS or dual-frequency GPS, and it is expected that all accuracy and protection level requirements will be met in that case.

A sensitivity analysis has been carried out to better characterize and account for the systematic errors associated with the many subsystems of the RALT Aiding architecture.

Errors such as the sensitivity to terrain variations between terrain elevation data points, the 139 sensitivity to lateral position errors, and the sensitivity to aircraft attitude have been examined. Additionally, an in-depth analysis of the characteristics of both LiDAR and radar generated terrain elevation databases have been presented and a comparison between the two completed. The sensitivity analysis concluded that:

i. The optimal post-spacing for terrain elevation databases for use with RALT

Aiding is believed to be 1.0 meters if a constant spacing database is used.

ii. Position estimate lateral offsets caused but GPS errors can introduce

significant vertical errors in the radar altimeter derived measurement,

systems such as SBAS can greatly reduce the lateral offset error.

iii. The lever arm error caused by the attitude of the aircraft is not expected to

introduce a large error given that the maximum offset is less than the

ARINC 707 (ref. [24]) radar altimeter error specification.

iv. It is not expected that the change in the radar altimeter illumination zone

due to the aircraft’s attitude will introduce a large measurement error given

the maximum pitch and roll of the aircraft during a precision approach is

less than the beamwidth of the radar altimeter antenna.

An analysis and improvement to the previously developed terrain database spot algorithm has also been completed. The spot algorithm has been shown to improve terrain database polling over certain terrains when compared to the plumb bob method. More studies are needed on this algorithm as the final implementation is dependent on the selected terrain elevation database, radar altimeter, and antenna pattern. 140

Finally, a short discussion on the approach for certification has been presented with comparisons to other certified aviation navigation systems. Developing the RALT Aiding architecture around previously certified systems should significantly expedite a certification process.

RALT Aiding provides a feasible solution to meet the navigation performance requirements to support a fully automatic precision approach and landing and forms an important step to solving the challenge of landing an RPA at a wide range of airports. Based on the current performance of SBAS, RALT Aiding can provide a near term navigation solution for the RPA landing issue given an accurate terrain elevation database. Future work will be needed to further refine and improve the architecture for certification, but this thesis provides a foundation on which additional development can build upon.

141

13 RECOMMENDATIONS FOR FUTURE WORK

Recommendations following this work include ideas for additional flight tests, different methods for computing the aircraft’s position, and modification the standard RPA approach if possible. Each of these recommendations are explored further in the following sections.

13.1 Additional Flight Tests

At the time of writing this thesis, no flight tests have been performed specifically for testing the RALT Aiding capabilities. The RALT Aiding flight tests analysis presented in this thesis are based on data from previous Ohio University flight tests originally designed for other purposes. Designing and conducting a flight test around the RALT

Aiding system and approach procedure will provide a more accurate evaluation of the architecture. This flight test can take place on a manned or unmanned aircraft, using a representative radar altimeter and GNSS SBAS receiver, to test RALT Aiding real time during an approach and record important data for further analysis and development. The required equipment on board the aircraft would be:

i. Dual-frequency or SBAS GNSS receiver

ii. Aviation grade radar altimeter

iii. Terrain Elevation Database

iv. Data processing computer

v. Inertial navigation system

The data needed for RALT Aiding that should also be recorded is (all timestamped): 142

i. GPS observation data: L1/L2/L5 pseudorange measurements, L1/L2/L5

carrier phase measurements, L1/L2/L5 doppler, and L1/L2/L5 signal

strength

ii. GPS navigation data (almanac and ephemeris): SV PRN, Epoch, TOC, SV

clock bias, SV clock drift, SV clock drift rate, IODE, Crs, Delta n, M0, Cuc,

E, Cus, Sqrt(a), TOE, Cic, Omega, CIS, I0, Crc, Omega, OMEGA DOT,

IDOT, GPS week, SV accuracy, SV health, TGD, IODC

iii. Radar altimeter data: Radar altimeter range measurement

iv. Aircraft attitude data: Pitch, Roll, and Heading

v. SBAS data: All received WAAS/EGNOS messages

Additionally, the location of the aircraft center of gravity, GPS antenna, and radar altimeter antenna need to be accurately measured to ensure correct lever arm corrections are applied.

13.2 Methods for Position Computation

Additional methods for computing the aircraft’s position should be explored, including ways to integrate the RALT measurement over a longer period-of-time. The work presented in this thesis only used the LL method for computing the aircraft’s position.

This method is only able to compute a position using measurements from a single time epoch. Other methods, such as a batch least squares and Kalman filters, could be better suited for this application as they are capable of incorporating multiple measurements over a given time-period, increasing the accuracy of the solution compared to the single measurement solution. 143

13.3 Modifications to RPA Approach

As with most precision approach and landing systems, the intention of RALT

Aiding is to accurately and safely guide the aircraft to the runway touchdown point following a standard 3-degree glideslope. While touching down the aircraft at the designated touchdown markings is the most common approach, according to FAA AC 120-

28D Criteria for Approval of Category III Weather Minima for Takeoff, Landing, and

Rollout (ref. [55]) and RTCA DO-245A Minimum Aviation System Performance Standards for the Local Area Augmentation System (LAAS) (ref. [10]) the aircraft’s landing gear must land no shorter than 200ft past the runway threshold and no longer than 2,700ft past the runway threshold. This landing zone is depicted in Figure 13-1.

Figure 13-1: CAT III Autoland Touchdown Performance Requirements (Derived from [10])

Given the accuracy of the range measurement provided by the radar altimeter would improve when flying over the level runway, landing further down the runway than the normal touchdown point would improve the RALT Aiding positional accuracy. According to the Central Intelligence Agency (CIA) world fact book, around 37% of the paved runways in the United States are over 5,000 feet long [56]. The General Atomics MQ-9 144

Predator is capable of landing at a runway 3,000 feet long [57], meaning it could land

2,000ft past the runway threshold of a 5,000ft long runway and still safely operate.

Compared to the normal touchdown point 1,000ft from the runway threshold, landing an additional 1,000ft down the runway would gain the aircraft an additional 50ft of altitude when crossing the runway threshold, and more time for the radar altimeter to accurate measure the distance between the runway and aircraft. This modified approach is illustrated in Figure 13-2.

Figure 13-2: Modified RPA Approach and Landing

145

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152

APPENDIX A: SINGLE-FREQUENCY GPS ERROR STATISTICS

COMPUTATION

To model the errors seen in single frequency GPS, the method defined in [18] is used. This method root sum squares (RSS) the User Range Error (URE) and User

Equipment Error (UEE) together to calculate the so-called User Equivalent Range Error

(UERE). The UERE value is assumed to be a 1-sigma total pseudorange accuracy and is then squared to find the total single frequency satellite error variance:

푈퐸푅퐸 = √푈퐸퐸2 + 푈푅퐸2 (49)

2 2 휎푖 = 푈퐸푅퐸푖 (50)

The selected value for URE and UEE are defined in [18] and are assumed to be the same for each SV and are independent of elevation angle. The selected values for URE and

UEE are presented in Table 7-1.

Table A-1: Single Frequency SV Errors [2] Error Sources Value User Range Error (URE) 12.8 meters 95% User Equipment Error 5.5 meters 95% (UEE)

153

APPENDIX B: DUAL-FREQUENCY GPS ERROR STATISTICS

COMPUTATION

Unlike the single frequency model where all GPS SVs have the same static error statistic, the dual frequency error model is more involved and accounts for atmospheric effects and noise effects based on the SVs elevation angle. The total single satellite error statistic standard deviation is calculated as both a nominal and worst-case (max) values and is given by:

2 2 2 휎푖,푛표푚 = √푈푅퐸 + 휎푛표푚푡푟표푝표 + 휎푛표푚퐷퐹 (51)

2 2 2 휎푖,푚푎푥 = √푈푅퐴 + 휎푚푎푥푡푟표푝표 + 휎푚푎푥퐷퐹 (52)

Where the tropospheric standard deviation, 휎푛표푚푡푟표푝표 and 휎푚푎푥푡푟표푝표, is calculated by:

(0.08)(1.001) 휎푛표푚푡푟표푝표 = (53) √0.002001 + 푠𝑖푛2(퐸퐿) (0.12)(1.001) 휎푚푎푥푡푟표푝표 = (54) √0.002001 + 푠𝑖푛2(퐸퐿)

where 퐸퐿 is the elevation angle between the user and GP V from the user’s perspective in degrees. The dual frequency standard deviation, 휎푛표푚퐷퐹 and 휎푚푎푥퐷퐹, is calculated by:

2 2 σ푛표푚퐷퐹 = 푎√σ푛표푚푛표푖푠푒 + σ푛표푚푚푝 (55)

2 2 σ푚푎푥퐷퐹 = 푎√σ푚푎푥푛표푖푠푒 + σ푚푎푥푚푝 (56)

with 푎 being equal to: 154

2 2 푓2 푓2 √ 1 2 (57) 푎 = ( 2 2) + ( 2 2) 푓1 − 푓2 푓1 − 푓2

Where 푓1 refers to the GSP L1 frequency of 1575.42 MHz and 푓2 refers to the GPS

L5 frequency of 1227.60 MHz. The error statistic for the standard deviation of the aircraft noise, 휎푛표푚푛표푖푠푒 and 휎푚푎푥푛표푖푠푒, is calculated as follows:

0.02(퐸퐿 − 5) σ = 0.04 − (58) 푛표푚푛표푖푠푒 85 0.02(퐸퐿 − 5) σ = 0.04 − (59) 푛표푚푛표푖푠푒 85

The error statistic standard deviation for the aircraft multipath, 휎푛표푚푚푝 and

휎푚푎푥푚푝, is computed by:

0.1(5 − 퐸퐿) σ = 0.18 − (60) 푛표푚푚푝 85 −0.1×퐸퐿 σ푚푎푥푚푝 = 0.1 + 0.5 × 푒 (61)

Finally, the value for the nominal bias, 푏푛표푚, is taken to be 0.1 meters and the value for the maximum bias, 푏푚푎푥, is taken to be 0.5 meters as defined in [37]. The values for

URA and URE are dependent on current situation and need to be determined at runtime or time of simulation.

155

APPENDIX C: WAAS ERROR STATISTICS COMPUTATION

The method to calculate the error statics when implementing WAAS are defined in

[11]. This method involves calculating error statistics for by (i) the variances of fast- and long-term correction residuals, (ii) the variance of the ionospheric delays, (iii) the variance of receiver errors, (iv) and the variance of tropospheric errors on a per satellite basis. Once individually calculated, the total per-satellite variance is calculated by:

2 2 2 2 2 σ푖 = σ푖,푓푙푡 + σ푖,푈퐼푅퐸 + σ푖,푎푖푟 + σ푖,푡푟표푝표 (62)

2 Where the variance of the fast- and long-term correction residuals, 휎푖,푓푙푡, is computed by:

2 2 σ푓푙푡 = ((σ푈퐷푅퐸 ) ∗ (δ푈퐷푅퐸) + 푐표푛푠푡푎푛푡 푡푒푟푚) (63)

2 The variance of the ionospheric delay, 휎푈퐼푅퐸 , is given by:

2 2 2 휎푈퐼푅퐸 = (휎푈퐼푉퐸 ) ∗ (퐹푝푝) (64)

Where 퐹푝푝, the obliquity factor, is calculated by:

1 (− ) 푅 푐표푠(퐸퐿) 2 2 푒 (65) 퐹푝푝 = (1 − ( ) ) 푅푒 + 퐻퐼

Where 푅푒, the appro im ate radius of the earth’s ellipsoid, is 6378.136 and ℎ퐼, the height of the maximum electron density, is 350.0 km [11].

The standard deviation of receiver errors, 휎푎푖푟, is calculated using: 156

2 2 2 σ푎푖푟 = √σ푛표푖푠푒 + σ푚푢푙푡푖푝푎푡ℎ + σ푑푖푣푔 (66)

The standard deviation of multipath, 휎푚푢푙푡푖푝푎푡ℎ, is solved using:

(−퐸퐿/10푑푒푔) σ푚푢푙푖푝푎푡ℎ = 0.1 + 0.5 푒 (67)

Finally, the variance of the tropospheric errors, 휎푡푟표푝표, is calculated using:

σ푡푟표푝표 = (σ푇푉퐸 ∗ 푚(퐸퐿)) (68)

Where 푚(퐸퐿) is calculated as:

1.001 푚(퐸퐿) = (69) √0.002001 + 푠𝑖푛2(퐸퐿)

2 2 The values for 휎푈퐷푅퐸 , 훿푈퐷푅퐸, 푐표푛푠푡푎푛푡 푡푒푟푚, 휎푈퐼푉퐸, 휎푑푖푣푔, and 휎푛표푖푠푒 are provided by the WAAS system during real time operation or selected by the user during simulation.

157

APPENDIX D: RAIM VPL AND HPL COMPUTATION

The RAIM computation of the HPL and VPL compares the position residuals from the all-in-view solution and a subset solution with the nth satellite omitted [15]:

푇 −1 푇 Δ퐱푛,푛표푚 = (퐇 퐌푛퐖푛표푚퐇) )퐇 퐌푛퐖푛표푚Δ퐲 (70)

푇 −1 푇 퐆푛,푛표푚 = (퐇 퐌푛퐖푛표푚퐇) 퐇 퐌푛퐖푛표푚 (71)

푇 −1 푇 퐆푎,푛표푚 = (퐇 퐖푛표푚퐇) 퐇 퐖푛표푚 (72)

th where 퐌푛 is an identity matrix with the n diagonal element replaced with a zero. The difference between the all-in-view and subset solution is calculated using:

Δ퐆푛,푛표푚 = 퐆푛,푛표푚 − 퐆푎,푛표푚 (73)

The detection statistic can be calculated using:

−1 푇 푑퐏푛 = Δ퐆푛,푛표푚퐖푛표푚Δ퐆푛,푛표푚 (74)

Then, the standard deviation for the nth satellite in the vertical direction is calculated by:

σ푑푉,푛 = √푑퐏푛( , ) (75)

where the detection threshold in the vertical direction is given by:

퐷푑푉,푛 = 퐾푓푓푑,푛 ∗ σ푑푉,푛 (76)

The vertical noise in the subset is calculated by: 158

σ푉,푛 = √퐏푛,푚푎푥( , ) (77)

where Pn,max is the covariance matrix of the faulted subset using the maximum error standard deviations:

푇 −1 퐏푛,푚푎푥 = (퐇 퐌푛퐖푚푎푥퐇) (78)

Finally, the VPL is calculated as:

푉푃퐿푓푎푢푙푡푒푑 = 푚푎푥(푉푃퐿푛) (79)

where the nth VPL is calculated as:

푉푃퐿푛 = 퐷푑푉,푛 + 퐾푚푑,푛 ∗ σ푉,푛 + ∑|푆푛,푚푎푥( , 𝑖)| ∗ 푏푚푎푥,푖 (80)

With 푆푛,푚푎푥 being calculated as:

푇 −1 푇 퐒푛,푚푎푥 = (퐇 퐌푛퐖푚푎푥퐇) 퐇 퐌푛퐖푚푎푥 (81)

The values for Kmd,n are based the total allowable probability of hazardous misleading information (Pr{HMI}) and the calculations are defined in [15].The Kmd,n values used in this study are presented in Table D-1 in terms of N number of satellites in view. 159

Table D-1: K-Multipliers for Faulted Protection Level Calculations Number of Satellites Kmd,n Kffd,n 5 2.05 5.07 6 2.13 5.10 7 2.19 5.13 8 2.24 5.16 9 2.29 5.18 10 2.33 5.20 11 2.36 5.22 12 2.39 5.23

The HPL is calculated in a similar manner to the VPL and is defined as [15]:

퐻푃퐿푓푎푢푙푡푒푑 = 푚푎푥(퐻푃퐿푛) (82)

where:

퐻푃퐿 = 퐷 + 퐾 ∗ σ + ∑ √푆2 (1, 𝑖) + 푆2 (2, 𝑖) 푛 푑퐻,푛 푚푑,푛 퐻,푛 푛,푚푎푥 푛,푚푎푥 (83) ∗ 푏푚푎푥,푖

The nth satellite detection threshold is calculated by:

퐷푑퐻,푛 = 퐾푓푓푑,푛 ∗ σ푑퐻,푛 (84)

where the horizontal nth satellite standard deviation is calculated by:

1 σ = √ (σ2 + σ2 + √(σ2 − σ2)2 + 4σ2 ) (85) 푑퐻,푛 2 1 2 1 2 12

2 2 with 휎1 , 휎2 , and σ12 coming from the dPn matrix as shown below: 160

2 σ1 σ12 σ13 σ14 2 σ21 σ2 σ23 σ24 푑퐏푛 = 2 (86) σ31 σ32 σ3 σ34 2 (σ41 σ42 σ43 σ4 )

161

APPENDIX E: WAAS VPL AND HPL COMPUTATION

For the work completed in this thesis, the HPL and VPL for WAAS is the same as the algorithm defined in RTCA DO-229D (ref. [11]) Appendix J. The HPL and VPL are calculated by [11]:

푉푃퐿푊퐴퐴푆 = 퐾푉 ∗ 푑푈 (87)

퐻푃퐿푊퐴퐴푆 = 퐾퐻,푃퐴 ∗ 푑푚푎푗표푟 (88)

With K vertical being 5.33 and K horizontal for a precision approach being 6.0. dU and dmajor are computed with the following equations [11]:

2 푑푒푎푠푡 푑퐸푁 푑퐸푈 푑퐸푇 2 푇 −1 푑퐸푁 푑푛표푟푡ℎ 푑푁푈 푑푁푇 퐃 = (퐇 퐖퐇) = 2 (89) 푑퐸푈 푑푁푈 푑푈 푑푈푇 2 [ 푑퐸푇 푑푁푇 푑푈푇 푑푇 ] 2 푑푢 = √푑푢 (90)

푑2 + 푑2 푑2 − 푑2 푑 = √ 푒푎푠푡 푛표푟푡ℎ + √( 푒푎푠푡 푛표푟푡ℎ) + 푑2 (91) 푚푎푗표푟 2 2 퐸푁

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