Exploiting Structure for Optimal Multi-Agent Bayesian Decentralized Estimation

TL;DR

This paper introduces an advanced Bayesian decentralized data fusion method that exploits probabilistic independence to compute tighter bounds and improve estimate accuracy in multi-agent systems.

Contribution

It proposes a novel non-monolithic covariance intersection algorithm and an optimization scheme to fully utilize dependency structures for better bounds.

Findings

Achieves tighter bounds than traditional CI in simulations.

Provides more accurate estimates in large-scale target tracking.

Converges to the same solution as existing methods in simple cases.

Abstract

A key challenge in Bayesian decentralized data fusion is the `rumor propagation' or `double counting' phenomenon, where previously sent data circulates back to its sender. It is often addressed by approximate methods like covariance intersection (CI) which takes a weighted average of the estimates to compute the bound. The problem is that this bound is not tight, i.e. the estimate is often over-conservative. In this paper, we show that by exploiting the probabilistic independence structure in multi-agent decentralized fusion problems a tighter bound can be found using (i) an expansion to the CI algorithm that uses multiple (non-monolithic) weighting factors instead of one (monolithic) factor in the original CI and (ii) a general optimization scheme that is able to compute optimal bounds and fully exploit an arbitrary dependency structure. We compare our methods and show that on a simple problem, they converge to the same solution. We then test our new non-monolithic CI algorithm on a large-scale target tracking simulation and show that it achieves a tighter bound and a more accurate estimate compared to the original monolithic CI.