Inhalt

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   Acknowledgments
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   Kurzfassung
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   Abstract
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   Contents
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   List of Figures
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   List of Tables
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   Nomenclatures
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  1 Introduction
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   1.1 Motivation
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   1.2 Previous Research
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  1.3 Research Area and Main Assumptions of the Thesis
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   1.3.1 Research Area
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   1.3.2 Main Assumptions of the Thesis
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   1.4 Thesis Contributions
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   1.5 Structure of the Thesis
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   2 Mobile Station Positioning Using GSM Networks
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   2.1 Overview of GSM Networks
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   2.2 Radio Propagation
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   2.3 Positioning Techniques and Measurements from GSM Networks
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   2.3.1 Time of Arrival
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   2.3.2 Time Difference of Arrival
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   2.3.3 Angle of Arrival
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   2.3.4 Received Signal Strength
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   2.3.5 Multipath Propagation
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   2.3.6 None-line-of-sight Propagation
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   2.3.7 Hearability Problem
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  2.4 Position Estimation
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   2.4.1 Static Estimation
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   2.4.2 Dynamic Estimation
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   2.5 Accuracy Criteria
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   2.5.1 Root Mean Square Error
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   2.5.2 FCC Requirements for E911
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   2.5.3 Geometric Dilution of Precision
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   2.6 Summary
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   3 A Data Fusion Solution for Ground Target Tracking
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  3.1 Target Dynamic Models
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   3.1.1 Nearly Constant Velocity Model
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   3.1.2 Nearly Constant Acceleration Model
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   3.1.3 Singer Model
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   3.1.4 Coordinated Turn Model
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   3.1.5 Curvilinear Model
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   3.2 State Estimation Using EKF
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   3.3 Posterior CRLB for Target Tracking
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  3.4 A Data Fusion Solution
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   3.4.1 Data Fusion Structure
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   3.4.2 Dynamic Model and Measurement Model for EKF
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  3.5 Simulation Results
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   3.5.1 Simulation Scenario
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   3.5.2 EKF Design
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   3.5.3 Performance Comparisons
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  3.6 PCRLB for the Data Fusion Solution
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   3.6.1 Derivation
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   3.6.2 Simulation Results
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   3.7 Summary
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  4 Road-Constrained Target Tracking
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   4.1 Road Information
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   4.2 Constrained State Estimation
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   4.2.1 Pseudomeasurement Approach
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   4.2.2 Projection Approach
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   4.2.3 Comparison of Pseudomeasurement and Projection Approach
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   4.3 Road Constraint as Pseudomeasurement: Linear Case
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   4.3.1 Position Estimation without Constraints
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   4.3.2 Road Constraints as Pseudomeasurements
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   4.3.3 EKF for Road-Constrained Tracking
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   4.3.4 Simulation Results
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  4.4 Road Constraint as Pseudomeasurement: Nonlinear Case
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   4.4.1 Formulation
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   4.4.2 EKF for Road-Constrained Tracking
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   4.4.3 Simulation Results
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   4.5 Summary
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   5 An Adaptive Road-Constrained IMM Estimator
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   5.1 Maneuvering Target Tracking
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  5.2 Interacting Multiple Model Estimator
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   5.2.1 Algorithm
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   5.2.2 Extended Kalman Filter in Subfilters
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   5.2.3 Simulation Results
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   5.3 An Adaptive Road-Constrained IMM Estimator
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   5.3.1 Ground Target Tracking on the Road
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   5.3.2 ARC-IMM Algorithm
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   5.3.3 Simulation Results
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   5.4 Summary
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  6 Conclusions and Outlook
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   6.1 Conclusions
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   6.2 Outlook
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  A Some Useful Formulae for Vectors and Matrices
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  A.1 Derivatives of Vectors and Matrices
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   A.1.1 The Gradient of a Scalar Function f(x)
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   A.1.2 The Gradient of a Vector-Valued Function f(x)
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   A.1.3 The Hessian of a Scalar Function f(x)
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   A.2 The Inversion of a Partitioned Matrix
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   A.3 Matrix Inversion Lemma
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   B Posterior Cramér-Rao Lower Bound for Nonlinear Filtering with Additive Gaussian Noise
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  C Derivations for Constrained State Estimation
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   C.1 Maximum Conditional Probability Method for Projection Approach
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   C.2 Mean Square Method for Projection Approach
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   C.3 Constrained Estimate in Terms of the Unconstrained Estimate Using Pseudomeasurement Approach
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   Bibliography