The problem of decision fusion in distributed sensor systems is considered. Distributed sensors pass their decisions about the same hypothesis to a fusion center that combines them into a final decision. Assuming that the sensor decisions are independent of each other for each hypothesis, the authors provide a general proof that the optimal decision scheme that maximizes the probability of detection at the fusion for fixed false alarm probability consists of a Neyman-Pearson test (or a randomized N-P test) at the fusion and likelihood-ratio tests at the sensors.<>
Optimal distributed decision fusion
IEEE Transactions on Aerospace and Electronic Systems ; 25 , 5 ; 761-765
1989-09-01
369536 byte
Article (Journal)
Electronic Resource
English
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