Graph-Based Compression for Distributed Particle Filters

A key challenge in designing distributed particle filters is to minimize the communication overhead without compromising tracking performance. In this paper, we present two distributed particle filters that achieve robust performance with low communication overhead. The two filters construct a graph of the particles and exploit the graph Laplacian matrix in different manners to encode the particle log-likelihoods using a minimum number of coefficients. We validate their performance via simulations with very low communication overhead and provide a theoretical error bound for the presented filters.