Distributed Detection and Data Fusion (Hardcover, 1997 ed.)

This book provides an introduction to decision making in a distributed computational framework. Classical detection theory assumes a centralized configuration. All observations are processed by a central processor to produce the decision. In the decentralized detection system, distributed detectors generate decisions based on locally available observations; these decisions are then conveyed to the fusion center that makes the global decision. Using numerous examples throughout the book, the author discusses such distributed detection processes under several different formulations and in a wide variety of network topologies.

Topics covered include: elements of detection theory; distributed Bayesian detection in parallel fusion networks and in other network topologies; distributed detection with false-alarm rate constraints; distributed sequential detection; and information theory and distributed hypotheses testing.

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.

General

Imprint:	Springer-Verlag New York
Country of origin:	United States
Release date:	December 1996
First published:	1997
Authors:	Pramod K. Varshney
Dimensions:	235 x 155 x 23mm (L x W x T)
Format:	Hardcover
Pages:	276
Edition:	1997 ed.
ISBN-13:	978-0-387-94712-9
Categories:	Books > Professional & Technical > Electronics & communications engineering > General Books > Computing & IT > General theory of computing > Mathematical theory of computation Books > Science & Mathematics > Mathematics > Applied mathematics > Mathematics for scientists & engineers Books > Computing & IT > Computer hardware & operating systems > Computer architecture & logic design > Parallel processing
LSN:	0-387-94712-4
Barcode:	9780387947129

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