Distributed data fusion using iterative covariance intersection

We propose an iterative extension of the covariance intersection (CI) algorithm for distributed data fusion. Our iterative CI (ICI) algorithm is able to disseminate local information throughout the network. We show that the ICI algorithm converges asymptotically to a consensus across all network nodes. We furthermore apply the ICI algorithm to distributed sequential Bayesian estimation and propose an ICI-based distributed particle filter (DPF). This DPF allows for spatially correlated measurement noises with unknown cross-correlations and does not require knowledge of the network size. The performance of the proposed DPF is assessed experimentally for a target tracking problem.