Distributed Estimation With Iterative Inverse Covariance Intersection

Abstract

For distributed estimation in sensor network systems, intractable correlation among local estimates has a great impact on the accuracy and consistency of the fused estimate. An inconsistent or over conservative estimate would lead to degradation of estimation performance or even estimation divergence. In this brief, a distributed iterative diffusion estimation algorithm based on inverse covariance intersection (ICI) technique is proposed to address the unknown correlation and yield an estimate with uncertainty close to the true mean square error. The proposed algorithm is referred to as iterative ICI (It-ICI), which leverages the iteration of fusions that is only executed locally. Every sensor node only combines estimates from its neighbors. By iterations, the local information can be propagated to the entire sensor network and the accuracy can be incrementally improved. The fused estimate is proved to be unbiased with bounded covariance provided that the local filters’ estimates are unbiased and bounded. The performance of the proposed distributed estimation algorithm is illustrated by a collaborative target tracking problem in terms of enhanced tracking accuracy and consistency.