Seamless group target tracking using random finite sets

2020 ◽  
Vol 176 ◽  
pp. 107683
Author(s):  
Zhejun Lu ◽  
Weidong Hu ◽  
Yongxiang Liu ◽  
Thia Kirubarajan
Keyword(s):  
Target Tracking ◽  
Finite Sets ◽  
Group Target ◽  
Random Finite Sets

scholarly journals Resolvable Group State Estimation with Maneuver Based on Labeled RFS and Graph Theory

Sensors ◽  
2019 ◽  
Vol 19 (6) ◽  
pp. 1307
Author(s):  
Weifeng Liu ◽  
Yudong Chi
Keyword(s):  
Graph Theory ◽  
State Estimation ◽  
Target Tracking ◽  
Finite Sets ◽  
Target Model ◽  
Group Members ◽  
Group Target ◽  
Random Finite Sets ◽  
Resolvable Group

In this paper, multiple resolvable group target tracking was considered in the frame of random finite sets. In particular, a group target model was introduced by combining graph theory with the labeled random finite sets (RFS). This accounted for dependence between group members. Simulations were presented to verify the proposed algorithm.

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.

scholarly journals Bayesian Multiple Extended Target Tracking Using Labeled Random Finite Sets and Splines

2018 ◽  
Vol 66 (22) ◽  
pp. 6076-6091 ◽  
Author(s):  
Abdullahi Daniyan ◽  
Sangarapillai Lambotharan ◽  
Anastasios Deligiannis ◽  
Yu Gong ◽  
Wen-Hua Chen
Keyword(s):  
Target Tracking ◽  
Finite Sets ◽  
Extended Target ◽  
Random Finite Sets
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