Distributed classification of multiple moving targets with binary wireless sensor networks

Abstract

Two sub-optimal decision fusion algorithms are presented in the context of distributed classification of multiple moving targets, as a low complexity alternative to the optimal decision fusion. At the fusion center, all the the binary decisions coming from a wireless sensor network (WSN) designed for single target classification are exploited for a multiple classification task. Based on the concept of maximum detection range of each sensor and approximating the joint posterior as a product of the posterior marginal, we derive the RLM (Range Limited Marginalization) and PRLM (Parallel Range Limited Marginalization) algorithms. Comparison between these suboptimal algorithms and the optimal decision fusion are performed for different scenarios, in terms of probabilities of detection and false alarm and metrics related to complexity theory.