Robust Distributed Fusion With Labeled Random Finite Sets

Li, S, Yi, W, Hoseinnezhad, R, Battistelli, G, Wang, B and Kong, L 2018, 'Robust Distributed Fusion With Labeled Random Finite Sets', IEEE Transactions on Signal Processing, vol. 66, no. 2, pp. 278-293.

Document type: Journal Article
Collection: Journal Articles

Title Robust Distributed Fusion With Labeled Random Finite Sets
Author(s) Li, S
Yi, W
Hoseinnezhad, R
Battistelli, G
Wang, B
Kong, L
Year 2018
Journal name IEEE Transactions on Signal Processing
Volume number 66
Issue number 2
Start page 278
End page 293
Total pages 16
Publisher IEEE
Abstract This paper considers the problem of the distributed fusion of multiobject posteriors in the labeled random finite set filtering framework, using a generalized covariance intersection (GCI) method. Our analysis shows that GCI fusion with labeled multiobject densities strongly relies on label consistencies between local multiobject posteriors at different sensor nodes, and hence suffers from a severe performance degradation when perfect label consistencies are violated. Moreover, we mathematically analyze this phenomenon from the perspective of the principle of minimum discrimination information and the so-called yes-object probability. Inspired by the analysis, we propose a novel and general solution for the distributed fusion with labeled multiobject densities that is robust to label inconsistencies between sensors. Specifically, the labeled multiobject posteriors are first marginalized to their unlabeled posteriors, which are then fused using the GCI method. We also introduce a principled method to construct the labeled fused density and produce tracks formally. Based on the developed theoretical framework, we present tractable algorithms for the family of generalized labeled multi-Bernoulli (GLMB) filters including ?-GLMB, marginalized ?-GLMB, and labeled multi-Bernoulli filters. The robustness and efficiency of the proposed distributed fusion algorithm are demonstrated in challenging tracking scenarios via numerical experiments.
Subject Signal Processing
Keyword(s) Marked point process
Multiobject tracking
Random finite set
Sensor networks
DOI - identifier 10.1109/TSP.2017.2760286
Copyright notice © 2017 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission.
ISSN 1053-587X
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Citation counts: TR Web of Science Citation Count  Cited 31 times in Thomson Reuters Web of Science Article | Citations
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