Multiple hypotheses testing strategy for distributed multisensor systems

We present a generalized multiple hypotheses testing strategy for a fully distributed multisensor system. Each sensor makes its own decision based on a set of rules which is derived by minimizing a Bayes cost risk function. The decision rules for each sensor are tightly coupled with other sensors since the “interdependence” between sensors is defined by the costs associated with each decision. For a special set of cost values, our proposed multiple hypotheses testing strategy becomes the common Bayes approach. We can also quantify the performance of the system in terms of the probabilities of correct, incorrect and uncertain decisions