Bayesian Multi-Target Tracking With Merged Measurements Using Labelled Random Finite Sets

2015 ◽  
Vol 63 (6) ◽  
pp. 1433-1447 ◽  
Author(s):  
Michael Beard ◽  
Ba-Tuong Vo ◽  
Ba-Ngu Vo
Keyword(s):  
Target Tracking ◽  
Finite Sets ◽  
Multi Target Tracking ◽  
Random Finite Sets

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

Sensors ◽  
2019 ◽  
Vol 19 (2) ◽  
pp. 366
Author(s):  
Han Shen-Tu ◽  
Hanming Qian ◽  
Dongliang Peng ◽  
Yunfei Guo ◽  
Ji-An Luo
Keyword(s):  
Target Tracking ◽  
Gaussian Mixture ◽  
Tracking Problem ◽  
Finite Sets ◽  
Probability Hypothesis Density ◽  
Multiple Sensors ◽  
Multi Target Tracking ◽  
Random Finite Sets

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.

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