Unifying consensus and covariance intersection for decentralized state estimation

This paper presents a new recursive information consensus filter for decentralized dynamic-state estimation. Local estimators are assumed to have access only to local information and no structure is assumed about the topology of the communication network, which need not be connected at all times. Iterative Covariance Intersection (ICI) is used to reach consensus over priors which might become correlated, while consensus over new information is handled using weights based on a Metropolis Hastings Markov Chain (MHMC). 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.