Set-Membership Constrained Particle Filter: Distributed Adaptation for Sensor Networks
Target tracking is investigated using particle filtering of data collected by distributed sensors. In lieu of a fusion center, local measurements must be disseminated across the network for each sensor to implement a centralized Particle Filter (PF). However, disseminating raw measurements incurs formidable communication overhead as large volumes of data are collected by the sensors. To reduce this overhead and thus enable distributed PF implementation, the present paper develops a Set-Membership Constrained (SMC) PF approach that exhibits performance comparable to the centralized PF; requires only communication of particle weights among neighboring sensors; and can afford both consensus-based and incremental averaging implementations.