Unifying Consensus and Covariance Intersection for Efficient Distributed State Estimation Over Unreliable Networks

Abstract

This article presents and studies a recursive information consensus filter for decentralized dynamic state estimation under circumstances in which the communication network is unreliable. 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. The filter is a hybrid approach: it uses iterative covariance intersection 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. We establish bounds for estimation performance and show that this hybrid method produces unbiased conservative estimates that are better than covariance intersection. The performance of the hybrid method is evaluated extensively, including comparisons with competing algorithms, with a hypothetical “full history” yardstick, and centralized performance. We conduct an assessment on a realistic atmospheric dispersion problem and also on more carefully crafted settings to help characterize particular aspects of the performance.