Distributed Detection and Data Fusion

by Varshney, Pramod K.; Burrus, C. S.

• ISBN13: 9780387947129
• ISBN10: 0387947124
• Format: Hardcover
• Copyright: 1997-06-01
• Publisher: Springer Verlag

Summary

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.

Table of Contents

Preface	ix
1 Introduction	1	(5)
1.1 Distributed Detection Systems	1	(3)
1.2 Outline of the Book	4	(2)
2 Elements of Detection Theory	6	(30)
2.1 Introduction	6	(1)
2.2 Bayesian Detection Theory	7	(7)
2.3 Minimax Detection	14	(2)
2.4 Neyman-Pearson Test	16	(2)
2.5 Sequential Detection	18	(6)
2.6 Constant False Alarm Rate (CFAR) Detection	24	(8)
2.7 Locally Optimum Detection	32	(4)
3 Distributed Bayesian Detection: Parallel Fusion Network	36	(83)
3.1 Introduction	36	(1)
3.2 Distributed Detection Without Fusion	37	(22)
3.3 Design of Fusion Rules	59	(13)
3.4 Detection with Parallel Fusion Network	72	(47)
4 Distributed Bayesian Detection: Other Network Topologies	119	(60)
4.1 Introduction	119	(1)
4.2 The Serial Network	120	(17)
4.3 Tree Networks	137	(2)
4.4 Detection Networks with Feedback	139	(20)
4.5 Generalized Formulation for Detection Networks	159	(20)
5 Distributed Detection with False Alarm Rate Constraints	179	(37)
5.1 Introduction	179	(1)
5.2 Distributed Neyman-Pearson Detection	180	(11)
5.3 Distributed CFAR Detection	191	(15)
5.4 Distributed Detection of Weak Signals	206	(10)
6 Distributed Sequential Detection	17	(216)
6.1 Introduction	216	(1)
6.2 Sequential Test Performed at the Sensors	217	(9)
6.3 Sequential Test Performed at the Fusion Center	226	(7)
7 Information Theory and Distributed Hypothesis Testing	233	(18)
7.1 Introduction	233	(1)
7.2 Distributed Detection Based on Information Theoretic Criterion	234	(11)
7.3 Multiterminal Detection with Data Compression	245	(6)
Selected Bibliography	251	(22)
Index	273