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arXiv · 1603.08340

Distributed Fusion with Multi-Bernoulli Filter based on Generalized Covariance Intersection

Bailu Wang·Wei Yi·Reza Hoseinnezhad·Suqi Li·Lingjiang Kong·Xiaobo Yang
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Abstract

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.

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Bailu Wang, Wei Yi, Reza Hoseinnezhad, Suqi Li, Lingjiang Kong, Xiaobo Yang. 2016-03-28. Distributed Fusion with Multi-Bernoulli Filter based on Generalized Covariance Intersection. https://doi.org/10.1109/tsp.2016.2617825

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