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

Institution of Information and Control, Hangzhou Dianzi University, Hangzhou 310018, China
Science and Technology on Near-surface Detection Laboratory, Wuxi 214035, China
Authors to whom correspondence should be addressed.
Sensors 2019, 19(2), 366;
Received: 23 October 2018 / Revised: 2 January 2019 / Accepted: 5 January 2019 / Published: 17 January 2019
(This article belongs to the Section Physical Sensors)
PDF [3806 KB, uploaded 17 January 2019]


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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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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