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• W3174889762 abstract "This paper addresses the problem of distributed multi-target tracking (MTT) in a radar network wherein both the positions and fields of view (FoVs) of each radar are unknown to the network. The probability hypothesis density (PHD) filters are running locally in each radar for MTT. The Weighted Arithmetic Average (WAA) fusion rule is employed because of its robustness to multi-sensor tracking with different FoVs. Due to the unknown FoVs of radars may be different and time-varying, in our method, the local intensity densities are decomposed into a number of sub-densities by a clustering algorithm. Then the positions of sensors are estimated by minimizing the Cauchy-Schwartz divergence (CSD) between the sub-densities which belong to the same cluster. Further, the WAA fusion is also performed in parallel using the sub-densities in each cluster and the fused intensity is constructed as the sum of the fused sub-intensities. The efficacy of the proposed method is demonstrated in a challenging tracking scenario via simulations." @default.
• W3174889762 created "2021-07-05" @default.
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• W3174889762 date "2021-05-07" @default.
• W3174889762 modified "2023-10-16" @default.
• W3174889762 title "Distributed Registration and Multi-target Tracking with Unknown Sensor Fields of View" @default.
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• W3174889762 doi "https://doi.org/10.1109/radarconf2147009.2021.9455171" @default.
• W3174889762 hasPublicationYear "2021" @default.
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