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Titlebook: Distributed Detection and Data Fusion; Pramod K. Varshney Book 1997 Springer Science+Business Media New York 1997 Signal.Topologie.data co

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发表于 2025-3-21 19:00:34 | 显示全部楼层 |阅读模式
书目名称Distributed Detection and Data Fusion
编辑Pramod K. Varshney
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图书封面Titlebook: Distributed Detection and Data Fusion;  Pramod K. Varshney Book 1997 Springer Science+Business Media New York 1997 Signal.Topologie.data co
描述This book provides an introductory treatment of the fundamentals of decision-making in a distributed framework. Classical detection theory assumes that complete observations are available at a central processor for decision-making. More recently, many applications have been identified in which observations are processed in a distributed manner and decisions are made at the distributed processors, or processed data (compressed observations) are conveyed to a fusion center that makes the global decision. Conventional detection theory has been extended so that it can deal with such distributed detection problems. A unified treatment of recent advances in this new branch of statistical decision theory is presented. Distributed detection under different formulations and for a variety of detection network topologies is discussed. This material is not available in any other book and has appeared relatively recently in technical journals. The level of presentation is such that the hook can be used as a graduate-level textbook. Numerous examples are presented throughout the book. It is assumed that the reader has been exposed to detection theory. The book will also serve as a useful referen
出版日期Book 1997
关键词Signal; Topologie; data compression; information; information theory
版次1
doihttps://doi.org/10.1007/978-1-4612-1904-0
isbn_softcover978-1-4612-7333-2
isbn_ebook978-1-4612-1904-0
copyrightSpringer Science+Business Media New York 1997
The information of publication is updating

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Elements of Detection Theory,l possibilities. For example, in a radar detection context, a decision is to be made regarding the presence or absence of a target based on the radar return. In a digital communication system, one of several possible waveforms is transmitted over a channel. Based on the received noisy observation, w
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Distributed Bayesian Detection: Parallel Fusion Network,e parallel fusion topology has received the most attention. In this chapter, we develop the theory of Bayesian detection for parallel fusion structures. In Section 3.2, we consider a parallel structure consisting of a number of detectors whose decisions are available locally and are not transmitted
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Distributed Bayesian Detection: Other Network Topologies,othesis testing for several other network topologies. In Section 4.2, we consider the serial or tandem network, a widely studied network topology. System design methodology is developed and its performance is compared with that of the parallel network. Interesting issues, such as sequencing and plac
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Distributed Sequential Detection,ctors. As observations continue to arrive, detectors include them in their decision making. Unlike fixed-sample-size detection problems where decisions are made after receiving the entire set of observations, sequential detectors may choose to stop at any time and make a final decision or continue t
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Information Theory and Distributed Hypothesis Testing,tion of statistical decision theory to the problem of determining the presence or absence of signals in noise. Both of these theories deal with the communication problem. The relationship between information theory and conventional (centralized) detection theory has been discussed in the literature.
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ssumes that complete observations are available at a central processor for decision-making. More recently, many applications have been identified in which observations are processed in a distributed manner and decisions are made at the distributed processors, or processed data (compressed observatio
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The State of the Environment in Asia a particular course of action from a set of possible options. This problem involving a single decision maker is typically a difficult one. Decision making in large-scale systems consisting of multiple decision makers is an even more challenging problem. Group decision-making structures are found in
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