Institution of Information and Control, Hangzhou Dianzi University, Hangzhou 310018, China.
Science and Technology on Near-surface Detection Laboratory, Wuxi 214035, China.
Sensors (Basel). 2019 Jan 17;19(2):366. doi: 10.3390/s19020366.
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.
在本文中，我们研究了随机有限集公式中的多传感器多目标跟踪问题。采用高斯混合概率假设密度（GM-PHD）方法来构建顺序融合多传感器 GM-PHD（SFMGM-PHD）算法。首先，将 GM-PHD 应用于多个传感器，以并行方式获得后验 GM 估计。其次，我们提出了 SFMGM-PHD 算法，以顺序方式融合多传感器 GM 估计。第三，进一步提出了不平衡加权融合和自适应序列排序方法，用于两种改进的 SFMGM-PHD 算法。最后，我们在四个不同的多传感器多目标跟踪场景中分析了所提出的算法，结果证明了其有效性。
