Exploiting Structure for Optimal Multi-Agent Bayesian Decentralized Estimation

• URL: http://arxiv.org/abs/2307.10594v1
• Date: Thu, 20 Jul 2023 05:16:33 GMT
• Title: Exploiting Structure for Optimal Multi-Agent Bayesian Decentralized Estimation
• Authors: Christopher Funk, Ofer Dagan, Benjamin Noack and Nisar R. Ahmed
• Abstract summary: Key challenge in Bayesian decentralized data fusion is the rumor propagation' or double counting' phenomenon. We show that by exploiting the probabilistic independence structure in multi-agent decentralized fusion problems a tighter bound can be found. 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.
• Score: 4.320393382724066
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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