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GNSS Receiver Architectures for Remote Sensing Applications

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GNSS Receiver Architectures for Remote Sensing Applications GNSS Receiver Architectures for Remote Sensing Applications by Sara J. Hrbek M.S., University of Colorado, 2016 M.S., Lule˚aUniversity of Technology, 2012 A thesis submitted to the Faculty of the Graduate School of the University of Colorado in partial fulfillment of the requirements for the degree of Doctor of Philosophy Department of Aerospace Engineering Sciences 2019 This thesis entitled: GNSS Receiver Architectures for Remote Sensing Applications written by Sara J. Hrbek has been approved for the Department of Aerospace Engineering Sciences Prof. Dennis M. Akos Prof. Penina Axelrad Date The final copy of this thesis has been examined by the signatories, and we find that both the content and the form meet acceptable presentation standards of scholarly work in the above mentioned discipline. Hrbek, Sara J. (Ph.D., Aerospace Engineering Sciences) GNSS Receiver Architectures for Remote Sensing Applications Thesis directed by Prof. Dennis M. Akos Global Navigation Satellite System (GNSS) signals designed to support position, navigation and time, can also be used as signals of opportunity for remote sensing applications. As GNSS evolve, the receiver architectures need to evolve as well. This work explores how to improve the GNSS receiver for remote sensing. Three areas are developed throughout the thesis: utilization of modernized signals, signal cancellation for reduction of cross-correlation noise, and development of a vector delay frequency lock loop with support for Low Earth Orbit (LEO). The improved structures of modernized signals offer considerable advantages for GNSS Re- flectometry. One of those signals is GPS L5, which features increased signal power and a higher chipping rate. A comparison study of the transmit power between GPS L1 and L5 showed that the transmit power difference for L5 is close to the specified 3.6 dB. Despite this, due to performance differences of the receive antennas, the benefit might not be obtained. The higher chipping rate of the L5 signal provide advantages in terms of increased waveform resolution for GNSS-R. A study of altimetry performance with WAAS L5 was undertaken. After optimizing the coherent integration time, the altitude standard deviation of the L5 signal, compared to the L1 signal was found to be 2.2 times smaller. The improvements were primarily seen when the aircraft changed altitude. Reducing cross-correlation noise by means of signal cancellation is an effective tool to im- prove weak signal tracking. Factors influencing performance of signal cancellation: filtering, analog quantization and processing resolution were studied. It was found that canceling a signal collected with a narrow filter bandwidth introduced a residual signal that contributed additional noise. A cancellation efficiency metric was developed in order to analyze how well a signal could be canceled. It was found that high cancellation efficiency could be obtained with 4 bit analog quantization and 9 bit processing resolution. iv GPS Radio Occultation measurements originate from the excess phase and amplitude of the occulted signal. The measurements can be obtained through open loop tracking, which requires precise orbit determination. The use of a vector delay frequency lock loop (VDFLL) with a low Earth orbit dynamic model is proposed to satisfy both of those requirements. The VDFLL takes advantage of inter-channel aiding for precision orbit determination as well as provides a natural extension to open loop processing. The algorithm was tested on simulated and live data, and was found to successfully track a low elevation signal from a spacecraft with geometric open loop processing through VDFLL. Dedication This thesis is dedicated to my husband Joe Hrbek. Thank you for your love, support and most of all because you never stopped believing I could finish it. vi Acknowledgements I would like to express my graditude to my advisor and committee chair Professor Dennis M. Akos, for guiding me throughout this process. I would also like to thank the other committee members: Professor Penina Axelrad, Professor Jade Morton, Professor Albin Gasiewski and Doctor Valery Zavorotny. Thank you to the sponsors of this work: NOAA, Iposi, IAI (GRIBSAR Project) and Space Science & Engineering. Thanks to the CYGNSS mission, lead by University of Michigan and sponored by NASA, for providing raw IF data files from the CYGNSS satellites. For help with data collections, writing papers and many many discussions about everything between Heaven and Earth: Staffan Back´en,Dahee Won, Nagaraj Shivaramaiah, Erin Griggs, Qiang Chen and Damian Miralles. Thank you! vii Contents Chapter 1 INTRODUCTION 1 1.1 Remote Sensing Using GNSS . 1 1.1.1 GNSS Reflectometry . 2 1.1.2 GPS Interferometric Reflectometry . 3 1.1.3 Radio Occultation . 4 1.1.4 Ionosphere Measurements . 4 1.2 GNSS Signals and Receiver Architecture . 6 1.2.1 Antenna . 9 1.2.2 Front-end . 10 1.2.3 Acquisition . 12 1.2.4 Tracking . 14 1.3 GNSS Software Receiver . 17 1.4 Research Outline . 18 1.5 Contributions . 19 2 BACKGROUND AND THEORY ON GNSS-R AND GPS RO 21 2.1 GPS/GNSS Reflectometry . 21 2.1.1 Observables . 22 2.1.2 Delay Waveforms . 22 viii 2.1.3 Delay Doppler Maps . 24 2.1.4 Bistatic Radar Model . 24 2.1.5 Altimetry . 28 2.1.6 Review of Research Studies Utilizing GNSS-R . 30 2.2 GPS/GNSS Radio Occultation . 31 2.2.1 Observables . 31 2.2.2 Bending Angle . 33 2.2.3 Atmospheric Parameters . 34 2.2.4 Radio Occultation Receiver . 35 2.2.5 Review of Research Studies Utilizing GPS RO . 39 2.3 Receiver Architecture Improvements . 40 3 UTILIZATION OF MODERNIZED SIGNALS FOR BISTATIC RADAR 41 3.1 GPS L5 signal structure . 42 3.2 GPS L5 Power Study . 43 3.2.1 Satellite Antenna Patterns . 43 3.2.2 Received Power Measurements . 45 3.3 GPS/GNSS Reflectometry Receiver Enhancements . 52 3.3.1 Software Receiver Architecture for Receiving L5 Reflected Signals . 53 3.3.2 Data Collection Setup and Pre-Processing . 55 3.3.3 Data Collection and Initial Results . 58 3.3.4 Optimization of L1 and L5 Signals . 60 3.4 Conclusions on Utilization of Modernized Signals for Bistatic Radar . 65 4 SUCCESSIVE INTERFERENCE CANCELLATION 68 4.1 Background . 69 4.1.1 Signal Cancellation . 69 4.1.2 Cross-Correlation in the GPS L1 C/A Signal . 70 ix 4.1.3 Filtering . 72 4.1.4 Quantization . 73 4.2 Detailed Analysis of Factors Influencing Signal Cancellation . 75 4.2.1 Signal Strength and Number of Satellites . 75 4.2.2 Analysis of the Effect of Filtering on Signal Cancellation . 76 4.2.3 Analysis of the Residual Signal Effect . 81 4.3 Analysis of the Effect of Processing Resolution on Signal Cancellation . 84 4.3.1 Simulation Analysis of Cancellation Efficiency . 88 4.3.2 Simulation for Comparison of Theoretical Calculations . 93 4.3.3 Cancellation of Multiple Signals . 94 4.4 Real Data Implementation of Signal Cancellation for Strong Signals . 96 4.5 Conclusions on Successive Interference Cancellation . 100 5 VECTOR DELAY FREQUENCY LOCK LOOP 103 5.1 Vector Lock Loop Tracking . 104 5.2 Traditional VDFLL operation . 106 5.2.1 Noise Matrices . 110 5.2.2 Traditional VDFLL Algorithm Verification . 111 5.3 Radio Occultation Position and Velocity Requirements . 113 5.4 VDFLL operation with LEO orbit dynamics . 115 5.4.1 Algorithm Validation . 119 5.4.2 VDFLL Operating on CYGNSS Data . 125 5.5 Utilizing VDFLL for an Adapted Closed/Open Loop tracking . 129 5.6 Conclusions on Vector Lock Loops for LEO Missions . 134 6 SUMMARY AND CONCLUSIONS 137 Bibliography 140 x Tables Table 1.1 GPS frequencies . 8 3.1 GPS L1 and L5 signal characteristics . 42 3.2 Front-end frequency plan . 57 3.3 Correlator chip spacing for L1 and L5 . 60 3.4 Calculated values of the correlation time based on Eq. 3.4 . 62 4.1 Analog quantization loss . 74 4.2 Bandwidth requirements . 84 4.3 Cancellation efficiency for N1 =2............................. 91 4.4 Cancellation efficiency for N1 =4............................. 92 4.5 Signals canceled . 96 4.6 Helix collected signals . 97 5.1 Tracked satellites during the Boulder Canyon Dr. drive test. 112 5.2 Summary of RO receiver navigation solution performance . 114 5.3 Processing stages . 121 5.4 Estimated position and velocity errors compared to the simulator truth. 125 5.5 CYGNSS spacecraft and orbit parameters. 126 5.6 CYGNSS dataset parameters. 127 5.7 CYGNSS data set acquisition parameters. 127 xi Figures Figure 1.1 GNSS signals . 7 1.2 Tracking . 11 1.3 Auto-correlation of PRN 3 . 13 1.4 Code tracking . 16 1.5 Block diagram of tracking . 17 2.1 Open loop processing . 23.
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