Cooperative Localization for Mobile Networks: A Distributed Belief Propagation - Mean Field Message Passing Algorithm

TL;DR

This paper introduces a hybrid message passing algorithm combining belief propagation and mean field methods for efficient distributed cooperative localization of mobile agents, achieving high accuracy with minimal communication overhead.

Contribution

It presents a novel hybrid message passing approach that reduces communication load while maintaining estimation accuracy in mobile network localization.

Findings

Low communication overhead with only three real values per message

Estimation accuracy comparable to particle-based belief propagation

Effective for distributed cooperative localization and tracking

Abstract

We propose a hybrid message passing method for distributed cooperative localization and tracking of mobile agents. Belief propagation and mean field message passing are employed for, respectively, the motion-related and measurement-related part of the factor graph. Using a Gaussian belief approximation, only three real values per message passing iteration have to be broadcast to neighboring agents. Despite these very low communication requirements, the estimation accuracy can be comparable to that of particle-based belief propagation.