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An Unbalanced Weighted Sequential Fusing Multi-Sensor GM-PHD Algorithm

1
Institution of Information and Control, Hangzhou Dianzi University, Hangzhou 310018, China
2
Science and Technology on Near-surface Detection Laboratory, Wuxi 214035, China
*
Authors to whom correspondence should be addressed.
Sensors 2019, 19(2), 366; https://doi.org/10.3390/s19020366
Received: 23 October 2018 / Revised: 2 January 2019 / Accepted: 5 January 2019 / Published: 17 January 2019
(This article belongs to the Section Physical Sensors)
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Abstract

In this paper, we study the multi-sensor multi-target tracking problem in the formulation of random finite sets. The Gaussian Mixture probability hypothesis density (GM-PHD) method is employed to formulate the sequential fusing multi-sensor GM-PHD (SFMGM-PHD) algorithm. First, the GM-PHD is applied to multiple sensors to get the posterior GM estimations in a parallel way. Second, we propose the SFMGM-PHD algorithm to fuse the multi-sensor GM estimations in a sequential way. Third, the unbalanced weighted fusing and adaptive sequence ordering methods are further proposed for two improved SFMGM-PHD algorithms. At last, we analyze the proposed algorithms in four different multi-sensor multi-target tracking scenes, and the results demonstrate the efficiency. View Full-Text
Keywords: random finite sets; multi-sensor multi-target tracking; multi-sensor data fusing; GM-PHD random finite sets; multi-sensor multi-target tracking; multi-sensor data fusing; GM-PHD
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Shen-Tu, H.; Qian, H.; Peng, D.; Guo, Y.; Luo, J.-A. An Unbalanced Weighted Sequential Fusing Multi-Sensor GM-PHD Algorithm. Sensors 2019, 19, 366.

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