# Exploiting Structure for Optimal Multi-Agent Bayesian Decentralized Estimation

arXiv: https://arxiv.org/abs/2307.10594 · HTML: https://mlsanity.com/paper/2307.10594/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 1 of its 18 references that are in the map.
- First posted on arXiv 20 Jul 2023.

## Builds on (most cited first, 1 of 1)

- [Exact and Approximate Heterogeneous Bayesian Decentralized Data Fusion](https://mlsanity.com/paper/2101.11116/) (2021)

## Abstract (arXiv)

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.