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

finite

organism

Distributed Bayesian Detection: Other Network Topologies,ls are presented that reduce this requirement. Finally, a unified methodology is presented to represent any decentralized detection network structure. Decision rules are also obtained. This methodology is applicable to detection networks that include memory as well as feedback.

BARK

虛假

Distributed Sequential Detection,decisions can still be combined using a fixed fusion rule. In Section 6.3, we consider a parallel fusion network consisting of . peripheral detectors and a fusion center. The local detectors send a sequence of summary messages to the fusion center where a sequential test is implemented. The fusion c

Enthralling

Information Theory and Distributed Hypothesis Testing,ensions of the above work for the distributed detection problem. In Section 7.2, we discuss the design of distributed detection systems based on an information theoretic cost function. In Section 7.3, we present a brief summary of some asymptototic results on the performance of distributed detection

construct

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偏見

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誘導(dǎo)

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牢騷

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Hippocampus

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