Likelihood Consensus-Based Distributed Particle Filtering with Distributed Proposal Density Adaptation

TL;DR

This paper introduces a distributed particle filtering approach for wireless sensor networks that employs likelihood consensus and adaptive proposal densities to improve global state estimation accuracy.

Contribution

It proposes a novel consensus-based distributed particle filter with adaptive proposal density adjustment, enhancing performance and efficiency in sensor network applications.

Findings

Effective distributed likelihood consensus scheme

Improved target tracking accuracy

Reduced number of particles needed for performance

Abstract

We present a consensus-based distributed particle filter (PF) for wireless sensor networks. Each sensor runs a local PF to compute a global state estimate that takes into account the measurements of all sensors. The local PFs use the joint (all-sensors) likelihood function, which is calculated in a distributed way by a novel generalization of the likelihood consensus scheme. A performance improvement (or a reduction of the required number of particles) is achieved by a novel distributed, consensus-based method for adapting the proposal densities of the local PFs. The performance of the proposed distributed PF is demonstrated for a target tracking problem.