Distributed Fusion With Multi-Bernoulli Filter Based on Generalized Covariance Intersection

In this paper, we propose a distributed multiobject 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...

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Bibliographic Details
Published in:IEEE transactions on signal processing 2017-01, Vol.65 (1), p.242-255
Main Authors: Bailu Wang, Wei Yi, Hoseinnezhad, Reza, Suqi Li, Lingjiang Kong, Xiaobo Yang
Format: Article
Language:English
Subjects:
Approximation algorithms
Closed-form solutions
Correlation
distributed fusion
Multi-Bernoulli filter
multi-target tracking
Radar tracking
Radio frequency
sensor network
Signal processing algorithms
Target tracking
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Summary:In this paper, we propose a distributed multiobject 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 problem, we first approximate the fused posterior as the unlabeled version of δ-generalized labeled MB distribution, referred to as generalized MB (GMB) distribution. Then, to allow the subsequent fusion with another MB 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.
ISSN:1053-587X
1941-0476
DOI:10.1109/TSP.2016.2617825