Optimal data fusion strategies using multiple-sensor detection systems

The problem of decision fusion in distributed sensor systems is considered. Distributed sensors pass their decisions to a fusion center that combines the received decisions from the various sensors into a final global decision. The case where only two sensors are combined using an AND fusion rule was analyzed by Kovattana (1973), and the cases where two and three sensors are combined using AND and OR fusion rules were analyzed by Fefjar (1978). Stearns (1983) considered the case of combining two sensors using AND and OR fusion rules and showed that the receiver operating characteristics of the AND and the OR combiners must be intersected, AND was superior to OR at low false-alarm probabilities, and OR was superior to AND at high false-alarm probabilities. This paper shows that the optimal fusion rule does not only depend on the desired false alarm probability and the signal to noise ratio but also depends on the probability distribution function.