FOUNDATIONS AND APPLICATIONS OF SENSOR MANAGEMENT (SIGNALS AND COMMUNICATION TECHNOLOGY)

HERO A.O. ET.AL

ISBN 10: 0387278923 / ISBN 13: 9780387278926

Published by SPRINGER, 2008

About this Item

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Bibliographic Details

Title: FOUNDATIONS AND APPLICATIONS OF SENSOR ...

Publisher: SPRINGER

Publication Date: 2008

Binding: Hardcover

Condition: New

About this title

Synopsis:

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.

From the Back Cover:

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.