An iterative method for decision fusion in multiple sensor systems

Distributed decision fusion has been intensively studied in the past. This interest has been sparked by the requirement of a distributed surveillance system to be more reliable and immune to electronic attack than a single sensor system. In this paper, we consider a binary decentralized distributed decision fusion in which a system of multiple independent sensors monitors a common volume and provides relevant binary decisions about the state of the environment to a data fusion center. The fusion center combines the binary decisions of the individual distributed sensors into a final global decision. We propose a simple iterative method for optimizing multiple sensor decision fusion systems, in terms of both the sensors and the fusion center. The proposed iterative method determines, for a given global false alarm probability, the corresponding optimum setting of the individual sensors thresholds and the fusion center combining strategy that yields maximum global detection probability. The proposed method enables efficient search for the optimum solution by starting from a variety of initial trial values. The performance of the proposed method is provided in case of Rayleigh distributed observations and is proved to be cost effective and efficient.