Distributed Fusion with Multi-Bernoulli Filter based on Generalized Covariance Intersection
It provides a practical distributed fusion solution for multi-object tracking with multi-Bernoulli filters, though the approximations may limit accuracy.
This paper proposes a distributed multi-object tracking algorithm using a multi-Bernoulli filter based on generalized Covariance Intersection, addressing the lack of an accurate closed-form fusion expression by approximating the fused posterior as a generalized multi-Bernoulli distribution and then as a multi-Bernoulli distribution. Numerical results demonstrate the algorithm's performance.
In this paper, we propose a distributed multi-object tracking algorithm through the use of multi-Bernoulli (MB) filter based on generalized Covariance Intersection (G-CI). Our analyses show that the G-CI fusion with two MB posterior distributions does not admit an accurate closed-form expression. To solve this challenging problem, we firstly approximate the fused posterior as the unlabeled version of $δ$-generalized labeled multi-Bernoulli ($δ$-GLMB) distribution, referred to as generalized multi-Bernoulli (GMB) distribution. Then, to allow the subsequent fusion with another multi-Bernoulli posterior distribution, e.g., fusion with a third sensor node in the sensor network, or fusion in the feedback working mode, we further approximate the fused GMB posterior distribution as an MB distribution which matches its first-order statistical moment. The proposed fusion algorithm is implemented using sequential Monte Carlo technique and its performance is highlighted by numerical results.