Distributed Detection and Data Fusion

Overview

This book provides an introduction to decision making in a distributed computational framework. When most computations were performed by a central processor, classical detection theory could assume that the processor could make decisions based on complete information. The development of distributed processors working in parallel on different parts of the same computational problem makes it necessary to make local decisions that are then conveyed to other processors, where ultimately a fusion center must make ...

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Paperback (Softcover reprint of the original 1st ed. 1997)
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Overview

This book provides an introduction to decision making in a distributed computational framework. When most computations were performed by a central processor, classical detection theory could assume that the processor could make decisions based on complete information. The development of distributed processors working in parallel on different parts of the same computational problem makes it necessary to make local decisions that are then conveyed to other processors, where ultimately a fusion center must make global decisions. Using numerous examples throughout the book, the author discusses such distributed detection processes under various different formulations and in a wide variety of network topologies. By providing a unified treatment of the recent advances, this book should prove valuable not only to researchers active in the field, but also to graduate students and others embarking on research in detection, signal processing, and statistical decision theory. Some prior knowledge of detection theory is assumed.

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Product Details

  • ISBN-13: 9781461273332
  • Publisher: Springer New York
  • Publication date: 12/31/2012
  • Edition description: Softcover reprint of the original 1st ed. 1997
  • Edition number: 1
  • Pages: 276
  • Product dimensions: 6.14 (w) x 9.21 (h) x 0.61 (d)

Table of Contents

1 Introduction.- 1.1 Distributed Detection Systems.- 1.2 Outline of the Book.- 2 Elements of Detection Theory.- 2.1 Introduction.- 2.2 Bayesian Detection Theory.- 2.3 Minimax Detection.- 2.4 Neyman-Pearson Test.- 2.5 Sequential Detection.- 2.6 Constant False Alarm Rate (CFAR) Detection.- 2.7 Locally Optimum Detection.- 3 Distributed Bayesian Detection: Parallel Fusion Network.- 3.1 Introduction.- 3.2 Distributed Detection Without Fusion.- 3.3 Design of Fusion Rules.- 3.4 Detection with Parallel Fusion Network.- 4 Distributed Bayesian Detection: Other Network Topologies.- 4.1 Introduction.- 4.2 The Serial Network.- 4.3 Tree Networks.- 4.4 Detection Networks with Feedback.- 4.5 Generalized Formulation for Detection Networks.- 5 Distributed Detection with False Alarm Rate Constraints.- 5.1 Introduction.- 5.2 Distributed Neyman-Pearson Detection.- 5.3 Distributed CFAR Detection.- 5.4 Distributed Detection of Weak Signals.- 6 Distributed Sequential Detection.- 6.1 Introduction.- 6.2 Sequential Test Performed at the Sensors.- 6.3 Sequential Test Performed at the Fusion Center.- 7 Information Theory and Distributed Hypothesis Testing.- 7.1 Introduction.- 7.2 Distributed Detection Based on Information Theoretic Criterion.- 7.3 Multiterminal Detection with Data Compression.- Selected Bibliography.

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