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The Always Best Positioned Paradigm for Mobile Indoor Applications

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The Always Best Positioned Paradigm for Mobile Indoor Applications The Always Best Positioned Paradigm for Mobile Indoor Applications Dissertation zur Erlangung des Grades des Doktors der Ingenieurwissenschaften (Dr.-Ing.) der Naturwissenschaftlich-Technischen Fakultaten¨ der Universitat¨ des Saarlandes vorgelegt von Tim Schwartz Saarbrucken¨ 4. Januar 2012 Dekan: Prof. Dr. Holger Hermanns Vorsitzender des Prufungsausschusses:¨ Prof. Dr. Philipp Slusallek Berichterstatter: Prof. Dr. Dr. h.c. mult. Wolfgang Wahlster Prof. Dr. Antonio Kruger¨ Promovierter akademischer Mitarbeiter der Fakultat:¨ Dr. Jorg¨ Baus Tag des Kolloquiums: 31. Januar 2012 Eidesstattliche Versicherung Hiermit versichere ich an Eides statt, dass ich die vorliegende Arbeit selbstandig¨ und ohne Benutzung anderer als der angegebenen Hilfsmittel angefertigt habe. Die aus anderen Quellen oder indirekt ubernommenen¨ Daten und Konzepte sind unter Angabe der Quelle gekennzeichnet. Die Arbeit wurde bisher weder im In- noch im Ausland in gleicher oder ahnlicher¨ Form in einem Verfahren zur Erlangung eines akademischen Grades vorgelegt. Tim Schwartz, Saarbrucken,¨ 4. Januar, 2012 iii Short Abstract In this dissertation, methods for personal positioning in outdoor and indoor envi- ronments are investigated. The Always Best Positioned paradigm, which has the goal of providing a preferably consistent self-positioning, will be defined. Further- more, the localization toolkit LOCATO will be presented, which allows to easily realize positioning systems that follow the paradigm. New algorithms were devel- oped, which particularly address the robustness of positioning systems with respect to the Always Best Positioned paradigm. With the help of this toolkit, three exam- ple positioning-systems were implemented, each designed for different applications and requirements: a low-cost system, which can be used in conjunction with user- adaptive public displays, a so-called opportunistic system, which enables positioning with room-level accuracy in any building that provides a WiFi infrastructure, and a high-accuracy system for instrumented environments, which works with active RFID tags and infrared beacons. Furthermore, a new and unique evaluation-method for po- sitioning systems is presented, which uses step-accurate natural walking-traces as ground truth. Finally, six location based services will be presented, which were realized either with the tools provided by LOCATO or with one of the example positioning-systems. iv Kurzzusammenfassung In dieser Doktorarbeit werden Methoden zur Personenpositionierung im Innen- und Außenbereich von Gebauden¨ untersucht. Es wird das ,,Always Best Positioned” Paradigma definiert, welches eine moglichst¨ luckenlose¨ Selbstpositionierung zum Ziel hat. Weiterhin wird die Lokalisierungsplattform LOCATO vorgestellt, welche eine einfache Umsetzung von Positionierungssystemen ermoglicht.¨ Hierzu wurden neue Algorithmen entwickelt, welche gezielt die Robustheit von Positionierungssys- temen unter Berucksichtigung¨ des ,,Always Best Positioned” Paradigmas angehen. Mit Hilfe dieser Plattform wurden drei Beispiel-Positionierungssysteme entwick- elt, welche unterschiedliche Einsatzgebiete berucksichtigen:¨ Ein kostengunstiges¨ System, das im Zusammenhang mit benutzeradaptiven offentlichen¨ Bildschirmen benutzt werden kann; ein sogenanntes opportunistisches Positionierungssystem, welches eine raumgenaue Positionierung in allen Gebauden¨ mit WLAN-Infrastruktur ermoglicht,¨ sowie ein metergenaues Positionierungssystem, welches mit Hilfe einer Instrumentierung aus aktiven RFID-Tags und Infrarot-Baken arbeitet. Weiterhin wird erstmalig eine Positionierungsevaluation vorgestellt, welche schrittgenaue, naturliche¨ Bewegungspfade als Referenzsystem einsetzt. Im Abschluss werden 6 lokationsbasierte Dienste vorgestellt, welche entweder mit Hilfe von LOCATO oder mit Hilfe einer der drei Beispiel-Positionierungssysteme entwickelt wurden. v Acknowledgments First and foremost, I want to express my gratitude to Prof. Wahlster for giving me the op- portunity and support to work on my PhD thesis. I truly enjoyed the times in which I could discuss my work and worries with him, and I am grateful for the encouragement, the good ideas and sound advice he has given to me. Although I learned a lot in the whole course of preparing and writing my PhD thesis, I feel that I learned the most when closely working with him. I also want to thank Prof. Slusallek for encouraging me to start a PhD thesis, as well as Prof. Kruger¨ and Prof. Butz who sparked my interest in Artificial Intelligence and who are great sources of inspiration. Likewise, I want to thank Christian Muller¨ who always had an open ear for my concerns. This work would not have been possible without a great working en- vironment with supportive, open minded and creative colleagues. It seems to me that the Chair of Prof. Wahlster and the IUI group at DFKI act as magnets for these special kind of people, who are usually hard to find: Doris Borchers, the good soul of the Chair, Jorg¨ Baus, the ‘Swiss Army knife’ and stabilizing element of the Chair who provided me with sound advice and encouragement, Christoph Stahl, with whom I worked closely to realize LOCATO and with whom I founded a start-up company, Boris Brandherm, with whom I developed and discussed the concept of geoDBNs, Mira Spassova and Gerrit Kahl, who incorporated LO- CATO into the IRL, Dominik Heckmann, who provided UBISWORLD and UBISEARTH, Ralf Jung, my long-term office mate who realized an location-adaptive audio notification service using LORIOT, Michael Schmitz, with whom I had the pleasure to supervise an interdisci- plinary course at the school of fine arts, Alexander Kroner,¨ Jens Haupert and Matthieu Deru with whom I supervised two Android seminars, Angela Mahr, Sandro Castronovo, Christoph Endres and Michael Feld, with whom I worked in the project simTD. Thank you for the great time! Of course I also want to thank my family for the love and support during my whole life and especially during the last months of this thesis, in which I was terribly absent-minded. Although my MacBook seemed to overheat a few times in the last couple of months, it is still the most reliable computing machine I ever had. So yeah, thank you Steve Jobs: Here’s to the crazy ones . vi vii Wo laufen sie denn? Wo laufen sie denn hin, mein Gott? Bernhard Victor (Vicco) Christoph Carl von Bulow¨ alias ”‘Loriot”’ - AUF DER RENNBAHN viii Contents I Introduction 1 1 Introduction and Motivation 3 1.1 Introduction . 5 1.1.1 Context-Aware Applications . 5 1.1.2 Outdoor Positioning . 6 1.1.3 Indoor Positioning . 7 1.1.4 Privacy Protection . 8 1.1.5 Design Criteria for Positioning Systems . 8 1.1.6 The Always Best Positioned Paradigm . 10 1.2 Research Questions . 11 1.3 Thesis Outline . 12 II Theoretical Part: Foundations 15 2 Background 17 2.1 The Advent of Position Awareness . 17 2.1.1 A Naturalistic Perspective . 17 2.1.1.1 Senses and Stimuli . 18 2.1.1.2 Combination of Senses . 19 ix x CONTENTS 2.1.1.3 Dead Reckoning . 20 2.1.1.4 Landmarks . 21 2.1.1.5 Non-Electronic Tools for Positioning . 22 2.2 Human and Artificial Agents . 23 2.2.1 Sensors and Senders . 25 2.3 Classification of Positioning Systems . 27 2.3.1 Indoor versus Outdoor Positioning . 27 2.3.2 Egocentric and Exocentric Positioning . 28 2.3.2.1 Exocentric Positioning . 29 2.3.2.2 Egocentric Positioning . 29 2.3.2.3 Hybrid Approaches . 29 2.3.2.4 Onboard and Offboard Calculation . 31 2.3.2.5 Discussion . 32 2.3.3 Instrumented Environments and Opportunistic Positioning Systems . 33 2.4 Position Representation . 34 2.4.1 World Geodetic System WGS84 . 34 2.4.2 Semantic Representation . 36 2.4.2.1 UBISWORLD ................... 37 2.4.3 Positioning in a Moving Reference System . 38 2.5 Basic Mathematical Principles of Positioning . 39 2.5.1 Trilateration and Multilateration . 39 2.5.1.1 Signal Strength . 40 2.5.1.2 Time of Arrival (TOA) . 41 2.5.1.3 Pseudorange . 41 2.5.1.4 Time Difference of Arrival (TDOA) . 42 2.5.1.5 Frequency Difference of Arrival (FDOA) . 44 2.5.2 Triangulation . 44 CONTENTS xi 2.5.2.1 Angle of Arrival (AOA) . 45 2.5.3 RSS Fingerprinting . 46 2.6 Methods for Sensor Fusion . 47 2.6.1 Kalman Filter . 48 2.6.2 Particle Filter . 51 2.6.3 Bayesian Networks . 54 2.6.3.1 Dynamic Bayesian Networks . 59 3 Related Work 61 3.1 Positioning with a Single Sensor Technology . 61 3.1.1 Global Navigation Satellite Systems (GNSS) . 61 3.1.1.1 NAVSTAR GPS . 61 3.1.1.2 GPS Indoors . 64 3.1.1.3 GLONASS . 65 3.1.1.4 Galileo . 65 3.1.1.5 BeiDou . 66 3.1.1.6 Pseudolites . 66 3.1.2 Cellular Based . 68 3.1.2.1 Cell ID (since 2G) . 70 3.1.2.2 Cell ID + Timing Advance (since 2G) . 70 3.1.2.3 Cell ID + Round Trip Time (since 3G) . 71 3.1.2.4 Observed Time Difference of Arrival (OTDOA) (since 2G) . 71 3.1.2.5 Angle of Arrival (AOA, only with additional hard- ware) . 72 3.1.2.6 Positioning in 4G . 72 3.1.2.7 Indoor Positioning with Femtocells and Picocells 74 3.1.3 WiFi Based . 75 3.1.4 Bluetooth Based . 78 xii CONTENTS 3.1.5 RFID Based . 80 3.1.6 Optical Positioning . 84 3.1.6.1 Infrared Based . 84 3.1.6.2 Camera Based . 87 3.1.6.3 Laser-Range Positioning . 90 3.1.6.4 Optical Positioning in Gaming Consoles . 94 3.1.7 Terrestrial Radio & TV Broadcast Based . 95 3.1.8 Magnetic Based . 97 3.1.9 Ultra-Wideband (UWB) Based . 98 3.1.10 Capacitance Based . 99 3.1.11 Wireless Sensor Networks (WSN) . 101 3.1.12 Sound Based . 103 3.1.12.1 Ultrasound Based . 104 3.1.12.2 Speaker Positioning . 106 3.2 Inertial Positioning . 108 3.3 Positioning with Several Sensor Technologies . 110 3.3.1 Pereira et al.: LocateMe . 110 3.3.2 Gallagher et al. 111 3.3.3 Peng et al.
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