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Distributed multi-sensor fusion using generalized multi-bernoulli densities | IEEE Conference Publication | IEEE Xplore

Distributed multi-sensor fusion using generalized multi-bernoulli densities


Abstract:

The paper addresses distributed multi-target tracking in the framework of generalized Covariance Intersection (GCI) over multistatic radar system. The proposed method is ...Show More

Abstract:

The paper addresses distributed multi-target tracking in the framework of generalized Covariance Intersection (GCI) over multistatic radar system. The proposed method is based on the unlabeled version of generalized labeled multi-Bernoulli (GLMB) family by discarding the labels, referred as generalized multi-Bernoulli (GMB) family. However, it doesn't permit closed form solution for GCI fusion with GMB family. To solve this challenging problem, firstly, we propose an efficient approximation to the GMB family which preserves both the probability hypothesis density (PHD) and cardinality distribution, named as second-order approximation of GMB (SO-GMB) density. Then, we derive explicit expression for the GCI fusion with SO-GMB density. Finally, we compare the first-order approximation of GMB (FO-GMB) density with SO-GMB density in two scenarios and make a concrete analysis of the advantages of the second-order approximation. Simulation results are presented to verify the proposed approach.
Date of Conference: 05-08 July 2016
Date Added to IEEE Xplore: 04 August 2016
ISBN Information:
Conference Location: Heidelberg, Germany

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