Consensus for Distributed Multi-Bernoulli Filter

This work investigates the consensus based distributed multi-object tracking with multi-Bernoulli (MB) filters. Consensus has emerged as a powerful tool for distributed computing, however, this technique suffers from the problem of the susceptibility to the double counting of common information. In this paper, based on our previous work, namely, distributed MB filter in the framework of Generalized Covariance Intersection (GCI), we exploit the consensus based distributed MB filter, referred to as C-GCI-MB fusion. Then we analyze how the C-GCI-MB fusion can avoid the double counting problem. Finally, we present the Gaussian mixture implementation of the proposed C-GCI-MB fusion. The performance advantages of the proposed fusion algorithm are demonstrated in a challenge multi-target tracking scenario.