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 need for a unified resource on nonlinear filtering, multitarget tracking, sensor management, and distributed fusion. Advanced Techniques in Tracking and Sensor Management: Theory and Applications offers an overview of the algorithms, frameworks, and applications shaping tracking and sensor research.
Drawing on four decades of research, the book is organized into four parts covering nonlinear filtering, multi-sensor and multitarget tracking, sensor management, and distributed fusion. Topics include particle filtering for constrained systems, random finite set–based multitarget tracking, scheduling and resource allocation, and consensus-based fusion. The book combines theory with applications in radar, surveillance, space situational awareness, and autonomous navigation. Chapters by international experts make complex concepts accessible to engineers, scientists, and graduate students.
Both a graduate-level textbook and a practitioner reference for defense, aerospace, and autonomous systems, this book:
- Features algorithms for space object tracking, debris detection, and SLAM using finite set statistics
- Covers Bayesian filtering and multitarget estimation in cluttered, uncertain environments
- Introduces approaches to satellite scheduling and power allocation for MIMO radar systems
- Explores distributed state estimation and multi-object density fusion across sensor networks
This book is essential reading for graduate courses in statistical signal processing, target tracking, and sensor management in electrical and computer engineering. It is also a valuable resource for engineers and researchers in aerospace, defense, autonomous systems, and surveillance.