Distributed fusion with multi-Bernoulli filter based on generalized Covariance Intersection

Abstract

In this letter, we consider the distributed multi-target tracking through the use of multi-Bernoulli based on generalized Covariance Intersection (G-CI). However, the G-CI fusion of two multi-Bernoulli posterior distributions does not admit an closed-form expression. To solve this problem, we firstly approximate the fused posterior as an unlabeled version of δ-generalized labelled multi-Bernoulli (δ-GLMB) distribution, referred to as δ-GMB. To allow the subsequent fusion with another multi-Bernoulli distribution, e.g., fusion with a third sensor node in the sensor network, or feedback working mode, we further approximate the fused δ-GMB posterior using a multi-Bernoulli formed distribution which matches its first-order statistical moment. We implement the proposed method using sequential Monte Carlo techniques and demonstrate its performance in two challenging tracking scenarios.