Distributed Multi-Sensor Fusion Using Generalized Multi-Bernoulli Densities

Jiang, M, Yi, W, Hoseinnezhad, R and Kong, L 2016, 'Distributed Multi-Sensor Fusion Using Generalized Multi-Bernoulli Densities', in 19th International Conference on Information Fusion, Heidelberg, Germany, 5-8 July 2016, pp. 1-8.


Document type: Conference Paper
Collection: Conference Papers

Title Distributed Multi-Sensor Fusion Using Generalized Multi-Bernoulli Densities
Author(s) Jiang, M
Yi, W
Hoseinnezhad, R
Kong, L
Year 2016
Conference name 19th International Conference on Information Fusion
Conference location Heidelberg, Germany
Conference dates 5-8 July 2016
Proceedings title 19th International Conference on Information Fusion
Publisher IEEE
Place of publication Heidelberg, Germany
Start page 1
End page 8
Total pages 8
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.
Subjects Signal Processing
ISBN 9780996452748
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