Decentralized State Estimation via a Hybrid of Consensus and Covariance intersection
This work addresses state estimation in decentralized networks for applications like environmental monitoring, though it appears incremental as it builds on existing consensus and Covariance Intersection methods.
The paper tackles decentralized dynamic-state estimation in networks with arbitrary topology and intermittent connectivity by proposing a hybrid consensus filter combining Covariance Intersection for priors and Metropolis Hastings weights for new information, resulting in unbiased conservative estimates that outperform Covariance Intersection in performance evaluations on an atmospheric dispersion problem.
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.