Synopses & Reviews
Over the past few years the problem of active sensing has received an increasing amount of attention from researchers in areas such as signal processing, automatic control, statistics, and machine learning. 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. In active sensing, the sequence of sensor actions, such as pointing angle, modality, or waveform, are selected adaptively based on information extracted from past measurements. When the adaptive selection rule is carefully designed such an on-line active approach to sensing can very significantly improve overall performance as compared to off-line approaches. However, due to the classic curse of dimensionality, design and implementation of optimal active sensing strategies has been and remain very challenging. 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.
Sensor management is 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. In sensor management the sequence of sensor actions, such as pointing angle, modality, or waveform, are selected adaptively based on information extracted from past measurements. This book presents the theory of sensor management with applications to real world examples such as adaptive mine detection, adaptive signal and image sampling, multiple target tracking, and radar waveform design. It is written by leading experts in the field for a diverse engineering audience ranging from signal processing, to automatic control, mathematical statistics, and machine learning. The level of treatment of the book is tutorial and self contained. The chapters of the book are grouped into three sections: theoretical foundations; approximate approaches; and applications. The book assumes the reader has a technical background at the level of a first year graduate student in one of the systems engineering disciplines, e.g. signal processing, control, or communications. An appendix is included on topics that the reader may not have seen as a first year graduate student such as: partially observable markov processes, statistical decision theory, information theory, and dynamic programming.
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.
This book covers control theory signal processing and relevant applications in a unified manner. It introduces the area, takes stock of advances, and describes open problems and challenges in order to advance the field. The editors and contributors to this book are pioneers in the area of active sensing and sensor management, and represent the diverse communities that are targeted.
Table of Contents
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 Learning.- Sensor Scheduling in Radar.- Defense Applications.