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An Unbalanced Weighted Sequential Fusing Multi-Sensor GM-PHD Algorithm
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DOI：10.3390/s19020366.png)
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Abstract
En 中文
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.
Keywords:
random finite sets
multi-sensor multi-target tracking
multi-sensor data fusing
GM-PHD
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