Table of Contents I. NONLINEAR FILTERING 1. Chapter 1 3D Tracking of an Aircraft Using Air Traffic Control 2D Radars Mahendra Mallick, Linfeng Xu, Xiaoqing Tian, and Jifeng Ru 1.1. Introduction 1.2. Measurement Model for Air Traffic Control 2D Radars 1.3.

Non-maneuvering Aircraft 1.4. Maneuvering Aircraft 1.5. Filter Evaluation Metrics 1.6. Simulations and Results: Non-maneuvering Motions 1.7.

Simulations and Results: Nearly Constant Turn 1.8. Conclusions References 2. Chapter 2 Reentry Vehicle Filtering Using Radar and Passive Angle-only Sensors Mahendra Mallick, Xiaoqing Tian, and Linfeng Xu 2.1. Introduction 2.2. Equation of Motion 2.

3. Itô Stochastic Differential Equation 2.4. Sensor Measurement Models 2.5. Range-Parametrization (RP) 2.6. Filter Initialization Using Radar Measurements 2.

7. Filter Initialization Using Passive Sensor Measurements 2.8. Filtering Algorithms 2.9. Numerical Simulation and Results 2.10. Conclusions References 3.

Chapter 3 Radar Tracking with Bias Estimation Ehsan Taghavi, Ratnasingham Tharmarasa, and T. Kirubarajan 3.1. Introduction 3.2. Target Motion Models and Clutter Model 3.3. Tracking with Radar Measurements 3.

4. Continuous 2-D Assignment 3.5. Multisensor Radar Tracking 3.6. Radar Bias Estimation 3.7. Simulation Results 3.

8. Conclusions References 4. Chapter 4 Exact IMM Estimation of Markov Switching Diffusions with Hybrid Jumps Henk Blom 4.1. Introduction 4.2. Markov Switching Diffusion with Hybrid Jumps 4.3.

Conditional Probability Mass-Density Given Continuous-Time Observations 4.4. IMM Estimation of MJLS with Hybrid Jumps 4.5. Particle Filtering of Markov Switching Diffusion with Hybrid Jumps 4.6. Conclusion References 5. Chapter 5 Model Joint Target Tracking and Intent Inference Using a Destination-Constrained Model Linfeng Xu, Peijie Yang, and Mahendra Mallick 5.

1. Introduction 5.2. Problem Formulation 5.3. Modeling of DC Dynamics 5.4. State Transition With Uncertain Arrival Time 5.

5. Estimation for DC Systems 5.6. Illustrative Examples and Discussions 5.7. Conclusion References 6. Chapter 6 Tracking Filter with Implicit Constraints Keyi Li and Gongjian Zhou 6.1.

Introduction 6.2. Tracking Filter with Destination Constraints 6.3. Tracking Filter with Trajectory Shape Constraints 6.4. Tracking Filter with Guidance Law Constraints 6.5.

Conclusions References 7. Chapter 7 Particle Filter Convergence: Various Notes Yvo Boers and Pranab K. Mandal 7.1. Introduction 7.2. Preliminaries 7.3.

Multimodality and the Particle Filter 7.4. Convergence of the PF-based Distribution 7.5. L1 Convergence of the PF-based a Posteriori Density 7.6. PF Convergence for Unbounded Test Functions 7.7.

Examples 7.7.1. Example 1 7.7.2. Example 2 7.7.

3. Example 3 7.7.4. Example 4 7.8. Conclusions References 8. Chapter 10 Posterior Cramér-Rao Bounds in Cluttered Environments with Measurement Origin and Accuracy Uncertainties Marcel Hernandez and Alfonso Farina 8.

1. Introduction 8.2. Posterior Cramér-Rao Lower Bound 8.3. Posterior Cramér -Rao Bound Approaches with Measurement Origin Uncertainty 8.4. Posterior Cramér-Rao Lower Bound Approaches with Intermittently Inflated Measurement Errors 8.

5. Posterior Cramér-Rao Lower Bound with Autocorrelated Multipath Measurements 8.6. Simulation Scenario 1 - Impact Point of a Ballistic Missile 8.7. Simulation Scenario 2 - Tracking a Ground-Based Vehicle in the Presence of Radar Spoofing 8.8. Simulation Scenario 3 - Tracking a Low-Flying Airborne Target in the Presence of Specular Multipath 8.

9. Conclusions References 9. Chapter 9 Tensor Decomposition in Point-Mass Filters O. Straka, J. Matousek, I. Puncochar, and J. Dunik 9.1.

Introduction 9.2. Model, Bayesian Estimation, and Numerical Solution 9.3. Overcoming PMF Complexity: Sparse, Smart, and Compressed 9.4. Tensor Decomposition of the Dynamic Model 9.5.

Tensor-Train Decomposition of Point-Mass Densities 9.6. Numerical Illustration 9.7. Conclusions References II. MULTTARGET TRACKING 10. Chapter 10 Bayesian Multitarget Tracking via Labeled Random Finite Set B.-N.

Vo, B.-T. Vo, T.T.D. Nguyen, C. Shim, and H.V.

Nguyen 10.1 Introduction 10.2. Bayesian Multitarget Tracking 10.3. LRFS Tracking Filters and Smoothers 10.4. MTT with Non-Standard Models 10.

5. Applications of LRFS MTT 10.6. Conclusions References 11. Chapter 11 Bayesian Track-Before-Detect for Airborne Maritime Radar Du Yong Kim, Branko Ristic, and Luke Rosenberg 11.1. Introduction 11.2.

Maritime Radar Data 11.3. Bernoulli TBD for Maritime Radar 11.4. A Multi-Target Bernoulli TBD Tracker 11.5. Exploiting Doppler in the Bernoulli TBD 11.6.

Bernoulli TBD for an Airborne Multichannel Radar 11.7. Conclusions References 12. Chapter 12 Moving Target Tracking Using ViSAR Imagery Xiaoqing Tian, Jing Liu, and Mahendra Mallick 12.1. Introduction 12.2. Tracking Algorithms 12.

3. Track Filtering 12.4. TBD Algorithms 12.5. CF Algorithms 12.6. Examples for TBD- and CF-based Tracking Methods 12.

7. Conclusions References 13. Chapter 13 Space Object Tracking Brandon A. Jones and Benjamin Reifler 13.1. Introduction 13.2. Modeling the Dynamics of Space Objects 13.

3. Observing Space Objects 13.4. Orbit Determination 13.5. Multitarget Tracking 13.6. Space Object Tracking 13.

7. Conclusions References 14. Chapter 14 Generalized Bernoulli Filters for Challenging Sensing Conditions Ronald Mahler 14.1. Introduction 14.2. Mathematical Background 14.3.

The Bernoulli Filter 14.4. Pairwise-Markov Bernoulli (PMB) Filter 14.5. The Dyadic Filter 14.6. Set-Valued Bernoulli Filters 14.7.

Mathematical Derivations 14.8. Conclusions References 15. Chapter 15 Space Surveillance via Poisson Labeled Multi-Bernoulli Tracking Martin Adams, Leonardo Cament, and Javier Correa 15.1. Introduction 15.2. A Brief Overview of SSA Research 15.

3. Track Initialization for Multiple Resident Space Objects 15.4. Poisson Labeled Multi-Bernoulli Filter 15.5. Resident Space Object (RSO) Motion Prediction Model 15.6. Resident Space Object (RSO) Measurement Model 15.

7. Multi-SO State Extraction 15.8. Multi-SO Filter Performance Metrics 15.9. Results 15.10. Conclusions References 16.

Chapter 16 Extended and Group Target Modeling and Estimation Weifeng Liu, Yun Zhu, and Xiaomeng Cao 16.1. Introduction 16.2. Problem Description 16.3. Extended/Group Target Tracking 16.4.

Conclusions References 17. Chapter 17 Random Finite Sets Meet Simultaneous Localization and Mapping Martin Adams, Felipe Inostroza, and Keith Leung 17.1. Introduction 17.2. A Brief History of SLAM 17.3. Bayesian-Based SLAM Fundamentals 17.

4. Relationships Between RV and RFS SLAM 17.5. Batch RFS-based SLAM Solutions 17.6. Conclusions References III. DISTRIBUTED FUSION 18. Chapter 18 Centralized and Distributed Multiple-Hypothesis Tracking Stefano Coraluppi 18.

1. Introduction 18.2. Multiple-Hypothesis Tracking 18.3. Distributed MHT 18.4. Target Localization with Angle-Only Measurements 18.

5. Target Localization with TOA Measurements 18.6. Decoupled Data Association and Track Management 18.7. Tracker Performance Modeling 18.8. Tracker Performance Metrics 18.

9. Conclusions References 19. Chapter 19 Distributed State Estimation on Sensor Networks Giorgio Battistelli, Luigi Chisci, and Nicola Forti 19.1. Introduction 19.2. Background 19.3.

Fusion of Probability Density Functions 19.4. Left Kullback-Leibler Fusion 19.5. Right Kullback-Leibler Fusion 19.6. Design of the Fusion Weights 19.7.

Scalable Fusion via Consensus 19.8. Distributed State Estimation 19.9. Numerical Simulation Examples 19.10. Conclusions Appendix 19.A Proof of Theorem 1 19.

B Proof of Theorem 2 19.C Proof of Theorem 3 19.D Proof of Theorem 4 19.E Proof of Theorem 5 19.F Proof of Theorem 6 References 20. Chapter 20 Fusion of Multiobject Densities Giorgio Battistelli, Luigi Chisci, Lin Gao, Amirali K. Gostar, and Reza Hoseinnezhad 20.1.

Introduction 20.2. Background on Multiobject Densities (MODs) 20.3. Information-Theoretic Criteria for MOD Fusion 20.4. Left Kullback-Leibler Fusion 20.5.

Right Kullback-Leibler Fusion 20.6. Numerical Simulation Examples 20.7. Complementary fusion for limited fields-of-view 20.8. Conclusions References IV. SENSOR MANAGEMENT 21.

Chapter 21 Agile and Non-Agile Multi-Satellite Scheduling Ratnasingham Tharmarasa, Abhijit Chatterjee, and Aranee Balachandran 21.1. Introduction 21.2. Satellite Scheduling: An Overview 21.3. Non-Agile Satellite Scheduling: Mix.