  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 $\delta$-generalized labeled multi-Bernoulli
($\delta$-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.
