Simultaneous Distributed Sensor Self-Localization and Target Tracking Using Belief Propagation and Likelihood Consensus

TL;DR

This paper presents a novel cooperative framework for sensor self-localization and target tracking that integrates belief propagation and likelihood consensus, enhancing performance in sensor networks without a central fusion node.

Contribution

It introduces CoSLAT, a unified distributed algorithm combining self-localization and target tracking using a factor graph and particle-based message passing, extending existing methods.

Findings

Significant improvements in localization accuracy.

Enhanced target tracking performance.

Effective decentralized algorithm without a fusion center.

Abstract

We introduce the framework of cooperative simultaneous localization and tracking (CoSLAT), which provides a consistent combination of cooperative self-localization (CSL) and distributed target tracking (DTT) in sensor networks without a fusion center. CoSLAT extends simultaneous localization and tracking (SLAT) in that it uses also intersensor measurements. Starting from a factor graph formulation of the CoSLAT problem, we develop a particle-based, distributed message passing algorithm for CoSLAT that combines nonparametric belief propagation with the likelihood consensus scheme. The proposed CoSLAT algorithm improves on state-of-the-art CSL and DTT algorithms by exchanging probabilistic information between CSL and DTT. Simulation results demonstrate substantial improvements in both self-localization and tracking performance.