Local-Vote Decision Fusion for Target Detection in Wireless Sensor Networks

Abstract

In this talk, we examine the problem of target detection by a wireless sensor network. Sensors acquire measurements emitted from the target that are corrupted by noise and initially make individual decisions about the presence/ absence of the target. We propose the Local-Vote Decision Fusion algorithm, in which sensors first correct their decisions using decisions of neighboring sensors, and then make a collective decision as a network. We show that, for a fixed system false alarm, this local correction achieves significantly higher target detection rate. We examine both distance- and nearest neighbor-based versions of the algorithm for grid and random sensor deployments. Further, an explicit formula that approximates the decision threshold for a given false alarm rate is derived, using limit theorems for random fields.