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.<>


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    Title :

    Optimal distributed decision fusion


    Contributors:


    Publication date :

    1989-09-01


    Size :

    369536 byte




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English