Contents -- About the Authors -- Preface -- Acknowledgements -- List of Abbreviations -- Notations -- 1 Introduction -- 1.1 Application Areas of Positioning (Chapter 2) -- 1.2 Basics of Wireless Communications for Positioning (Chapter 3) -- 1.3 Fundamentals of Positioning (Chapter 4) -- 1.4 Data Fusion and Filtering Techniques (Chapter 5) -- 1.5 Fundamentals of Tracking (Chapter 6) -- 1.6 Error Mitigation Techniques (Chapter 7) -- 1.7 Positioning Systems and Technologies (Chapter 8) -- 1.8 Cooperative Mobile Positioning (Chapter 9) -- 2 Application Areas of Positioning -- 2.1 Introduction -- 2.2 Localization Framework -- 2.3 Location-based Services -- 2.3.1 LBS ecosystem -- 2.3.2 Taxonomies -- 2.3.2.1 Application categories -- 2.4 Location-based Network Optimization -- 2.4.1 Radio network planning -- 2.4.2 Radio resource management -- 2.4.2.1 Beamforming -- 2.4.2.2 Power control -- 2.4.2.3 Packet scheduling -- 2.4.2.4 Handover -- 2.5 Conclusions -- 3 Basics of Wireless Communications for Positioning -- 3.1 Introduction -- 3.2 Radio Propagation -- 3.2.1 Path loss -- 3.2.2 Shadowing -- 3.2.3 Small-scale fading -- 3.2.3.1 Multipath fading -- 3.2.4 Radio propagation and mobile positioning -- 3.2.4.1 Measurements -- 3.2.4.2 Position estimation -- 3.2.4.3 NLOS positioning error mitigation -- 3.2.5 RSS-based positioning -- 3.3 Multiple-antenna Techniques -- 3.3.1 Spatial diversity -- 3.3.2 Spatial multiplexing -- 3.3.3 Gains obtained by exploiting the spatial domain -- 3.3.3.1 Array gain -- 3.3.3.2 Diversity gain -- 3.3.3.3 Multiplexing gain -- 3.3.3.4 Interference reduction -- 3.3.4 MIMO and mobile positioning -- 3.4 Modulation and Multiple-access Techniques -- 3.4.1 Modulation techniques -- 3.4.1.1 OFDM -- 3.4.1.2 Spread spectrum -- 3.4.2 Multiple-access techniques -- 3.4.2.1 TDMA -- 3.4.2.2 FDMA/OFDMA -- 3.4.2.3 CDMA -- 3.4.2.4 SDMA.

3.4.2.5 CSMA/CA -- 3.4.3 OFDMA and mobile positioning -- 3.5 Radio Resource Management and Mobile Positioning -- 3.5.1 Handoff, channel reuse and interference adaptation -- 3.5.1.1 Handoff prioritization -- 3.5.1.2 Channel reuse and interference adaptation -- 3.5.1.3 Predictive channel reservation -- 3.5.2 Power control -- 3.6 Cooperative Communications -- 3.6.1 RSS-based cooperative positioning -- 3.7 Cognitive Radio and Mobile Positioning -- 3.8 Conclusions -- 4 Fundamentals of Positioning -- 4.1 Introduction -- 4.2 Classification of Positioning Infrastructures -- 4.2.1 Positioning-system topology -- 4.2.2 Physical coverage range -- 4.2.3 Integration of positioning solutions -- 4.3 Types of Measurements and Methods for their Estimation -- 4.3.1 Cell ID -- 4.3.2 Signal strength -- 4.3.3 Time of arrival -- 4.3.4 Time difference of arrival -- 4.3.5 Angle of arrival -- 4.3.6 Personal-information identification -- 4.4 Positioning Techniques -- 4.4.1 Proximity sensing -- 4.4.1.1 Physical contact -- 4.4.1.2 Identity methods -- 4.4.1.3 Macropositioning -- 4.4.2 Triangulation -- 4.4.2.1 Lateration -- 4.4.2.2 Hyperbolic localization -- 4.4.2.3 Angulation -- 4.4.3 Fingerprinting -- 4.4.3.1 Calibration phase for database creation -- 4.4.3.2 Image/video approaches -- 4.4.3.3 Collaborative approach for database maintenance -- 4.4.4 Dead reckoning -- 4.4.5 Hybrid approaches -- 4.4.5.1 Hybrid angulation and lateration -- 4.4.5.2 Hybrid angulation and hyperbolic localization -- 4.5 Error Sources in Positioning -- 4.5.1 Propagation -- 4.5.1.1 Non-line-of-sight -- 4.5.1.2 Multipath fading -- 4.5.1.3 Shadowing -- 4.5.1.4 Body shadowing -- 4.5.1.5 Interference -- 4.5.1.6 The ionosphere -- 4.5.2 Geometry -- 4.5.3 Equipment and technology -- 4.6 Metrics of Location Accuracy -- 4.6.1 Circular error probability -- 4.6.2 Dilution of precision.

4.6.3 Cramér-Rao lower bound (CRLB) -- 4.7 Conclusions -- 5 Data Fusion and Filtering Techniques -- 5.1 Introduction -- 5.2 Least-squares Methods -- 5.2.1 Linear least squares -- 5.2.2 Recursive least squares -- 5.2.3 Weighted nonlinear least squares -- 5.2.3.1 Example of application -- 5.2.4 The absolute/local-minimum problem -- 5.3 Bayesian Filtering -- 5.3.1 The Kalman filter -- 5.3.1.1 Extended Kalman filter -- 5.3.1.2 Unscented Kalman filter -- 5.3.1.3 Convergence issues -- 5.3.2 The particle filter -- 5.3.3 Grid-based methods -- 5.4 Estimating Model Parameters and Biases in Observations -- 5.4.1 Precalibration -- 5.4.2 Joint parameter and state estimation -- 5.5 Alternative Approaches -- 5.5.1 Fingerprinting -- 5.5.2 Time series data -- 5.5.2.1 Single exponential smoothing -- 5.5.2.2 The double exponential smoother -- 5.6 Conclusions -- 6 Fundamentals of Tracking -- 6.1 Introduction -- 6.2 Impact of User Mobility on Positioning -- 6.2.1 Localizing static devices -- 6.2.2 Added complexity in tracking -- 6.2.3 Additional knowledge in cooperative environments -- 6.3 Mobility Models -- 6.3.1 Conventional models -- 6.3.2 Models based on stochastic processes -- 6.3.2.1 Brownian-motion model -- 6.3.2.2 Random walk model -- 6.3.2.3 Waypoint random walk -- 6.3.2.4 Gauss-Markov model -- 6.3.2.5 Models based on Markov chains -- 6.3.3 Geographical-restriction models -- 6.3.3.1 Pathway mobility model -- 6.3.4 Group mobility models -- 6.3.4.1 Reference point group mobility model -- 6.3.4.2 Correlation group mobility model -- 6.3.5 Social-based models -- 6.3.5.1 Model based on a sociability factor -- 6.4 Tracking Moving Devices -- 6.4.1 Mitigating obstructions in the propagation conditions -- 6.4.2 Tracking nonmaneuvering targets -- 6.4.3 Tracking maneuvering targets -- 6.4.3.1 Process adaptation using maneuver detection -- 6.4.3.2 Multiple-model approaches.

6.4.4 Learning position and trajectory patterns -- 6.4.4.1 The expectation maximization algorithm -- 6.4.4.2 The k-means algorithm -- 6.5 Conclusions -- 7 Error Mitigation Techniques -- 7.1 Introduction -- 7.2 System Model -- 7.2.1 Maximum-likelihood algorithm for LOS scenarios -- 7.2.2 Cramér-Rao lower bounds for LOS scenarios -- 7.3 NLOS Scenarios: Fundamental Limits and ML Solutions -- 7.3.1 ML-based algorithms -- 7.3.2 Cramér-Rao lower bound -- 7.4 Least-squares Techniques for NLOS Localization -- 7.4.1 Weighted least squares -- 7.4.2 Residual-weighting algorithm -- 7.5 Constraint-based Techniques for NLOS Localization -- 7.5.1 Constrained LS algorithm and quadratic programming -- 7.5.2 Linear programming -- 7.5.3 Geometry-constrained location estimation -- 7.5.4 Interior-point optimization -- 7.6 Robust Estimators for NLOS Localization -- 7.6.1 Huber M-estimator -- 7.6.2 Least median squares -- 7.6.3 Other robust estimation options -- 7.7 Identify and Discard Techniques for NLOS Localization -- 7.7.1 Residual test algorithm -- 7.8 Conclusions -- 8 Positioning Systems and Technologies -- 8.1 Introduction -- 8.2 Satellite Positioning -- 8.2.1 Overview -- 8.2.2 Basic principles -- 8.2.2.1 Mathematical background -- 8.2.3 Satellite positioning systems -- 8.2.3.1 Introductory remarks -- 8.2.3.2 The Global Positioning System -- 8.2.3.3 Augmentation systems -- 8.2.3.4 GPS III and GALILEO -- 8.2.4 Accuracy and reliability -- 8.2.5 Drawbacks when applied to mobile positioning -- 8.3 Cellular Positioning -- 8.3.1 Overview -- 8.3.2 GSM -- 8.3.2.1 Cell ID -- 8.3.2.2 RSSI -- 8.3.2.3 Mobile-assisted TOA -- 8.3.2.4 Accuracy and reliability -- 8.3.3 UMTS -- 8.3.3.1 3GPP standardization -- 8.3.3.2 OTDOA-IPDL -- 8.3.3.3 U-TDOA -- 8.3.3.4 A-GNSS-based positioning -- 8.3.4 Emergency applications in cellular networks.

8.3.5 Drawbacks when applied to mobile positioning -- 8.4 Wireless Local/Personal Area Network Positioning -- 8.4.1 Solutions on top of wireless local networks -- 8.4.1.1 UWB -- 8.4.1.2 Bluetooth -- 8.4.1.3 WLAN (Wi-Fi) -- 8.4.2 Dedicated solutions -- 8.4.2.1 RFID -- 8.4.2.2 Infrared -- 8.4.2.3 Ultrasound -- 8.5 Ad hoc Positioning -- 8.6 Hybrid Positioning -- 8.6.1 Heterogeneous positioning -- 8.6.2 Cellular and WLAN -- 8.6.3 Assisted GPS -- 8.7 Conclusions -- 9 Cooperative Mobile Positioning -- 9.1 Introduction -- 9.2 Cooperative Localization -- 9.2.1 Robot networks -- 9.2.2 Wireless sensor networks -- 9.2.2.1 Clustering -- 9.2.3 Wireless mobile networks -- 9.3 Cooperative Data Fusion and Filtering Techniques -- 9.3.1 Coop-WNLLS: Cooperative weighted nonlinear least squares -- 9.3.1.1 Example of application -- 9.3.2 Coop-EKF: Cooperative extended Kalman filter -- 9.3.2.1 Example of application -- 9.4 COMET: A Cooperative Mobile Positioning System -- 9.4.1 System architecture -- 9.4.2 Data fusion methods -- 9.4.2.1 1L-DF: One-level data fusion -- 9.4.2.2 2L-DF: Two-level data fusion -- 9.4.3 Performance evaluation -- 9.4.3.1 Simulation models -- 9.4.3.2 Simulation results -- 9.5 Conclusions -- References -- Index.