Geolocation techniques principles and applications (Springer, 2013)
Includes bibliographical references and index
Machine generated contents note: 1.Introduction -- 1.1.Overview -- 1.1.1.Outdoor Localization -- 1.1.2.Indoor Localization -- 1.2.Organization -- 1.2.1.Chapter 2: Localization in Harsh Multipath Environments -- 1.2.2.Chapter 3: Multipath and NLOS Mitigation Algorithms -- 1.2.3.Chapter 4: Fingerprinting Techniques -- 1.2.4.Chapter 5: Cellular Localization Systems -- 1.2.5.Chapters 6 and 7: Cooperative Localization in Wireless Sensor Networks-Centralized and Distributed Algorithms -- 1.2.6.Chapters 8 and 9: Inertial Navigation Systems -- References -- 2.Ranging and Localization in Harsh Multipath Environments -- 2.1.Basics of Geolocation -- 2.1.1.TOA-Based Techniques -- 2.1.2.TDOA Techniques -- 2.1.3.AOA-Based Techniques -- 2.1.4.Received Signal Strength Localization -- 2.2.The Multipath Problem -- 2.2.1.TOA-Based Ranging in LOS Multipath Channels -- 2.2.2.RSS-Based Ranging in LOS Multipath Environments -- 2.3.The NLOS Problem --
Contents note continued: 2.3.1.TOA-Based Ranging in NLOS Multipath Environments -- 2.3.2.RSS-Based Ranging in NLOS Multipath Environments -- 2.4.Empirical Evaluation of the Multipath and NLOS Problems -- 2.4.1.Channel Measurement Systems -- 2.4.2.Alavi Models -- 2.4.3.Alsindi Models -- 2.5.Conclusion -- References -- 3.Multipath and NLOS Mitigation Algorithms -- 3.1.Multipath Mitigation -- 3.1.1.Super-Resolution Technique: MUSIC Algorithm -- 3.1.2.Ultra Wideband Technology -- 3.2.NLOS Identification and Mitigation -- 3.2.1.NLOS Identification Techniques -- 3.2.2.NLOS Mitigation Algorithms -- 3.3.Conclusion -- References -- 4.Survey-Based Location Systems -- 4.1.Analytical Models -- 4.1.1.A Stochastic Model for the Similarity Metric -- 4.1.2.A Stochastic Model for the Correct Localization -- 4.2.Memoryless Systems -- 4.2.1.The Weighted k-Nearest Neighbors Method -- 4.2.2.Support Vector Machines -- 4.2.3.Neural Networks -- 4.2.4.Bayesian Inference -- 4.2.5.Comparison of Methods --
Contents note continued: 4.3.Memory Systems -- 4.3.1.Bayesian Inference in Memory Systems -- 4.3.2.Grid-Based Markov Localization -- 4.4.Channel Impulse Response Fingerprinting -- 4.4.1.Mapping Using a Neural Network -- 4.4.2.Mapping Using a Gaussian Kernel -- 4.4.3.Variations of CIR Fingerprinting -- 4.5.Non-Radio Frequency Features -- 4.5.1.Sound Features -- 4.5.2.Motion Features -- 4.5.3.Color Features -- 4.5.4.Connectivity Features -- 4.6.Remarks -- References -- 5.Cellular Localization -- 5.1.Motivation -- 5.2.Cellular Networks -- 5.2.1.Cellular Network Structure -- 5.2.2.Cellular Positioning Methods -- 5.3.GSM, WCDMA and LTE Cellular Networks -- 5.3.1.GSM Cellular Networks -- 5.3.2.WCDMA Cellular Networks -- 5.3.3.3GPP LTE Cellular Networks -- 5.4.Conclusions -- References -- 6.Cooperative Localization in Wireless Sensor Networks: Centralized Algorithms -- 6.1.Multilateration -- 6.1.1.Atomic Multilateration -- 6.1.2.Collaborative Multilateration --
Contents note continued: 6.2.Convex Optimization -- 6.2.1.Distance Constraints -- 6.2.2.Angular Constraints -- 6.3.Semi-Definite Programming -- 6.4.Linear Programming -- 6.4.1.Triangle Inequality Constraints -- 6.4.2.Location Reconstruction -- 6.4.3.Anchor Nodes -- 6.5.Multidimensional Scaling -- 6.5.1.Principal Component Analysis -- 6.5.2.Computing the Centralized Inner Product Matrix -- 6.6.Monte Carlo Localization -- References -- 7.Cooperative Localization in Wireless Sensor Networks: Distributed Algorithms -- 7.1.Theoretical Bounds for Centralized and Distributed Cooperative Versus Non-Cooperative Positioning -- 7.2.Distributed Positioning Algorithms -- 7.3.Distributed Network Error Propagation -- 7.3.1.Belief Propagation -- 7.3.2.Correctness of Belief Propagation: Double Counting Problem -- 7.3.3.Non-parametric Belief Propagation -- 7.4.Link Selection -- 7.4.1.Practical Application: Firefighters -- 7.5.Conclusions -- References -- 8.Inertial Systems --