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Explores advanced tracking, sensor management, and distributed fusion for modern engineering applications
Advances in statistical signal processing, sensor networks, and control theory have created a pressing need for a unified resource that synthesizes the latest developments in nonlinear filtering, multitarget tracking, sensor management, and distributed fusion. Advanced Techniques in Tracking and Sensor Management: Theory and Applications provides a carefully structured overview of the state-of-the-art algorithms, mathematical frameworks, and real-world applications that define the current landscape of tracking and sensor research.
Unifying four decades of research, the book is divided into four parts, each focusing on a central domain: nonlinear filtering, multi-sensor and multitarget tracking, sensor management, and distributed fusion. Topics include particle filtering methods for constrained systems, random finite set-based multitarget tracking, advanced scheduling and resource allocation strategies for sensor management, and consensus-based techniques for distributed fusion. These areas are covered with both theoretical rigor and practical clarity, highlighting their application to radar systems, intelligence and surveillance operations, and space situational awareness. Each in-depth chapter, authored by leading international experts, balances mathematical exposition with illustrative applications to make complex concepts accessible to engineers, scientists, and graduate students.
Both a textbook for advanced study and a reference for practitioners working at the forefront of defense, aerospace, and autonomous systems, Advanced Techniques in Tracking and Sensor Management:
Features cutting-edge algorithms for space object tracking, space debris detection, and SLAM with finite set statistics
Provides rigorous treatments of Bayesian filtering and multitarget estimation methods under cluttered, and uncertain environments
Introduces new approaches to multi-agile satellite scheduling and robust power allocation for MIMO radar systems
Explores distributed state estimation and multi-object density fusion across sensor networks
Advanced Techniques in Tracking and Sensor Management: Theory and Applications is essential reading for graduate-level courses in statistical signal processing, target tracking, and sensor management within electrical and computer engineering programs. It is also a key reference for engineers and researchers working in aerospace, defense, autonomous systems, and surveillance industries.
Contents
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: Mixed Open-and-Closed Loop
21.4. Multi-Agile Satellites Scheduling: Small Tasks
21.5. Multi-Agile Satellites Scheduling: Large Tasks
21.6. Conclusions
References
22. Chapter 22 Sensor Control for Multitarget Tracking: A Review
Amirali K. Gostar, Aidan Blair, Brank Ristic , and Reza Hoseinnezhad Introduction
22.1. Introduction
22.2. Understanding the Sensor Control Problem
22.3. Sensor Control Categorisation
22.4. In-Depth Analysis of Sensor Control Techniques
22.5. Selective Sensor Control
22.6. Extension to Multi-Sensor Control
22.7. Distributed Multi-Sensor Control
22.8. Comparative Analysis and Simulation Results
22.9. Limitations and Practical Considerations
22.10. Conclusions
References
23. Chapter 23 Optimal Sensor Placement for AOA Localization and Tracking
Kutluyil Dogancay and Hatem Hmam
23.1. Introduction
23.2. Bayesian Estimation Fundamentals
23.3. Optimality Criteria for Sensor Placement
23.4. Optimization Problem for Sensor Placement
23.5. Far-Field vs Near-Field Target
23.6. Optimal Bayesian Sensor Placement Results
23.7. Optimal Sensor Control for Target Tracking
23.8. Conclusions
References
24. Chapter 24 Robust Power Allocation for Multi-Target Tracking in Colocated MIMO Radars with Minimized Resource Consumption
Ye Yuan, Wei Yi, Reza Hoseinnezhad, and Pramod K. Varshney
24.1. Introduction
24.2. System Model for Colocated MIMO Radar with MTT
24.3. Closed-Form Data Processing for Cognitive MTT
24.4. Robust Resource Allocation for C-MIMO Radar
24.5. Numerical Simulations
24.6. Conclusions
References
25. Chapter 25 Multisensor Resource Allocation for Multitarget Tracking
Wenqiang Pu, Junkun Yan, Peng Zhang, Hao Jiao, and Hongwei Liu
25.1. Introduction
25.2. Resource Allocation Mechanism
25.3. Radar Resource
25.4. Tracking Performance Metric
25.5. Resource Allocation Models
25.6. Conclusions
References
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