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Development and Implementation of a Peer-To-Peer Kalman Filter for Pedestrian and Indoor Navigation

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Development and Implementation of a Peer-To-Peer Kalman Filter for Pedestrian and Indoor Navigation Development and Implementation of a Peer-to-Peer Kalman Filter for Pedestrian and Indoor Navigation Doktorarbeit Institut für Raumfahrttechnik und Weltraumnutzung Isabelle Krämer Development and Implementation of a Peer-to-Peer Kalman Filter for Pedestrian and Indoor Navigation Doktorarbeit Von Isabelle Krämer Institut für Raumfahrttechnik und Weltraumnutzung 7. Dezember 2012 ii Acknowledgements This work could not have been realized without the help of my current and former colleagues at the Institute. I want especially thank Dr. Thomas Pany who had an immense impact on the realization of my, at first, very basic idea of the P2P Kalman filter and Prof. Eissfeller who supported my process and gave me the opportunity to develop my own ideas. A very special thank goes to my students Paul and Tobias who did a great job implementing parts of the prototype and devoting more time than required for this work. For the constant encouragement to work on my thesis and to eventually finish it coming from Roland, Thomas and Victoria I was and still am very grateful. I also want to thank all my other colleagues for their help and support during my time at the Institute. Without the support and encouragement of my family namely my parents, my brother and his family I would not have been able to realize this work. The most important person in my life – Christian - I owe most of my gratitude for his constant support and patience. iii iv Abstract Smartphones are an integral part of our society by now. They are used for messaging, searching the Internet, working on documents, and of course for navigation. Although smartphones are also used for car navigation their main area of application is pedestrian navigation. Almost all smartphones sold today comprise a GPS L1 receiver which provides position computation with accuracy between 1 and 10 m as long as the environment in beneficial, i.e. the line-of-sight to satellites is not obstructed by trees or high buildings. But this is often the case in areas where smartphones are used primarily for navigation. Users walk in narrow streets with high density, in city centers, enter, and leave buildings and the smartphone is not able to follow their movement because it loses satellite signals. The approach presented in this thesis addresses the problem to enable seamless navigation for the user independently of the current environment and based on cooperative positioning and inertial navigation. It is intended to realize location-based services in areas and buildings with limited or no access to satellite data and a large amount of users like e.g. shopping malls, city centers, airports, railway stations and similar environments. The idea of this concept was for a start based on cooperative positioning between users’ devices denoted here as peers moving within an area with only limited access to satellite signals at certain places (windows, doors) or no access at all. The devices are therefore not able to provide a position by means of satellite signals. Instead of deploying solutions based on infrastructure, surveying, and centralized computations like range measurements, individual signal strength, and similar approaches a decentralized concept was developed. This concept suggests that the smartphone automatically detects if no satellite signals are available and uses its already integrated inertial sensors like magnetic field sensor, accelerometer, and gyroscope for seamless navigation. Since the quality of those sensors is very low the accuracy of the position estimation decreases with each step of the user. To avoid a continuously growing bias between real position and estimated position an update has to be performed to stabilize the position estimate. This update is either provided by the computation of a position based on satellite signals or if signals are not available by the exchange of position data with another peer in the near vicinity using peer-to-peer ad-hoc networks. The received and the own position are processed in a Kalman Filter algorithm and the result is then used as new position estimate and new start position for further navigation based on inertial sensors. The here presented concept is therefore denoted as Peer-to- Peer Kalman Filter (P2PKF). One aspect of this thesis is to develop and implement a prototype of the P2PKF for an actual smartphone. But different issues have to be examined before being able to provide a useful prototype implementation. It is therefore first analyzed if there are already similar approaches regarding indoor positioning and cooperative positioning. Although indoor positioning is an important topic for quite a long time (maybe since the first mass-market receivers were sold) a satisfying solution could not be found yet. Only recently some approaches came up which addressed indoor positioning in the context of cooperative positioning strategies. These are examined as well as already existing approaches based on inertial sensors for pedestrian navigation with mass-market smartphones. A dead reckoning algorithm which detect steps, estimates the step length and the heading is introduced based on the work which has already been done in this area of application. The next step is then to analyze the impact of the filtering algorithm on the position accuracy. Two variations of the standard Kalman Filter are examined and implemented to process simulations. Different scenarios are developed based on the variation of simulation parameters like the amount of peers, sensor accuracy and maximum distance to peers. These scenarios help to obtain the impact of the different parameters and of the filtering algorithms on the position estimation accuracy. Based on the results of these simulations a Kalman Filter algorithm can be implemented for the actual prototype. The last step to complete the prototype is the choice of a suitable communication standard to enable the P2P communication. Essential aspects are the pervasiveness of this standard in common smartphones v and the ability to autonomously establish temporary connections between peers without relying on available infrastructure like access points. Existing standards are therefore examined on their fulfillment of those characteristics. A prototype as it is intended for this work is composed of these three parts: navigation by means of inertial sensors, filtering algorithm, and communication. These three parts are implemented in a certain type of Java for Android smartphones. Each part is tested separately on its functionality and in combination with the other modules. At the end the whole prototype is examined in a real-life scenario. The results of these tests are analyzed and discussed in detail regarding issues like performance, functionality, and also power consumption. vi vii viii TABLE OF FIGURES ..................................................................................................................... XIII LIST OF TABLES.......................................................................................................................... XVII 1. INTRODUCTION .......................................................................................................................... 1 1.1 INTRODUCTION TO THE PEER-TO-PEER KALMAN FILTER ....................................................... 2 1.2 REQUIREMENTS OF THE APPROACH ........................................................................................... 4 1.3 OUTLINE ....................................................................................................................................... 5 2. PEDESTRIAN INDOOR POSITIONING AND NAVIGATION .............................................. 7 2.1 TERMINOLOGY AND REQUIREMENTS ......................................................................................... 8 2.1.1 TERMINOLOGY ............................................................................................................................. 8 2.1.2 ACCURACY REQUIREMENTS FOR INDOOR POSITIONING AND NAVIGATION ................................. 9 2.2 SATELLITE NAVIGATION - GPS AND GNSS ............................................................................. 10 2.2.1 OVERVIEW OF GNSS ................................................................................................................. 10 2.2.2 SYSTEM OVERVIEW ................................................................................................................... 11 2.2.3 POSITIONING WITH SATELLITE SYSTEMS ................................................................................... 12 2.2.4 RECEIVER FUNCTIONS ............................................................................................................... 13 2.2.5 GPS / GNSS FOR INDOOR POSITIONING ..................................................................................... 15 2.3 ASSISTED GPS (AGPS) .............................................................................................................. 18 2.3.1 OVERVIEW ................................................................................................................................. 18 2.3.2 INDOOR POSITIONING WITH AGPS ............................................................................................ 20 2.4 POSITIONING BASED ON MOBILE COMMUNICATION NETWORKS ............................................ 23 2.4.1 POSITIONING BASED ON NETWORK STRUCTURE ........................................................................ 23 2.4.2 POSITIONING BASED ON TIME-RANGING INFORMATION ...........................................................
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