Asynchronous distributed detection

Binary parallel distributed detection architectures use a bank of local detectors to observe a common volume of surveillance, and form binary local decisions about the existence or nonexistence of a target in that volume. It is assumed that the number of local decisions observed by the central detector, the data fusion center (DFC), within any observation period is Poisson distributed. The optimal fusion rule is developed, and is shown to be a generalization of the rule developed by Chair and Varshney for the synchronous system. The corresponding sufficient statistic is a weighted sum of the local decisions collected by the DFC within the observation interval. The weights are functions of the individual local detector performance probabilities (i.e., probabilities of false alarm and detection). Exact expressions and asymptotic approximations are developed for the performance of the DFC. These expressions are in terms of the local detector performance probabilities and the parameters of the Poisson distribution describing the local decision rates. They allow performance prediction and assessment of tradeoffs in the design of realistic decision fusion architectures.<
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