Active sensing is recognized as an enabling technology for the next generation of agile, multi-modal, and multi-waveform sensor platforms to efficiently perform tasks such as target detection, tracking, and identification. Recently, several research programs at DARPA (SWARMS, ISP), ARO (MURI), and AFOSR (ATR MURI) have funded efforts in areas related to active sensing. These resulted in focused efforts by several research groups in academia, government laboratories, and industry. These efforts have led to advances in theory and implementation that have borne some fruit in specific technology areas. For example, several promising new methods to approximate optimal multistage sensor management strategies for target tracking have been developed and an understanding of design challenges and performance tradeoffs is beginning to emerge. This book introduces the area, takes stock of these advances, and describes open problems and challenges in order to advance the field.

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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.

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