Decentralized State Estimation via a Hybrid of Consensus and Covariance intersection

TL;DR

This paper introduces a decentralized recursive information consensus filter combining consensus and covariance intersection, capable of handling dynamic, intermittently connected networks for improved state estimation.

Contribution

It proposes a novel hybrid filter that does not assume network structure and maintains unbiased conservative estimates, outperforming traditional covariance intersection methods.

Findings

Produces unbiased conservative estimates

Handles dynamic, intermittently connected networks

Outperforms traditional covariance intersection

Abstract

This paper presents a new recursive information consensus filter for decentralized dynamic-state estimation. No structure is assumed about the topology of the network and local estimators are assumed to have access only to local information. The network need not be connected at all times. Consensus over priors which might become correlated is performed through Covariance Intersection (CI) and consensus over new information is handled using weights based on a Metropolis Hastings Markov Chains. We establish bounds for estimation performance and show that our method produces unbiased conservative estimates that are better than CI. The performance of the proposed method is evaluated and compared with competing algorithms on an atmospheric dispersion problem.