Overview of Book -- Stochastic Control Theory for Sensor Management -- Information Theoretic Approaches to Sensor Management -- Joint Multi-Target Particle Filtering -- Pomdp Approximation Using Simulation and Heuristics -- Multi-Armed Bandit Problems -- Application of Multi-Armed Bandits to Sensor Management -- Active Learning and Sampling -- Plan-In-Advance Active Learning 0f Classifiers -- Application of Sensor Scheduling Concepts to Radar -- Defense Applications -- Appendices.

Foundations and Applications of Sensor Management presents the emerging theory of sensor management with applications to real-world examples such as landmine detection, adaptive signal and image sampling, multi-target tracking, and radar waveform scheduling. It is written by leading experts in the field for a diverse engineering audience ranging from signal processing, to automatic control, statistics, and machine learning. The level of treatment of the book is tutorial and self-contained. The chapters of the book follow a logical development from theoretical foundations to approximate approaches and ending with applications. The coverage includes the following topics: stochastic control foundations of sensor management; multi-armed bandits and their connections to sensor management; information-theoretic approaches; managed sensing for multi-target tracking; approximation methods based on embedded simulation; active learning for classification and sampling; and waveform scheduling for radar. An appendix is included to provide essential background on topics the reader may not have encountered as a first-year graduate student: Markov decision processes; information theory; and stopping times. Foundations and Applications of Sensor Management is an important reference for signal processing and control engineers and researchers as well as machine learning application developers.