Random Finite Sets and Gaussian Mixture Probability Hypothesis Density Filter in Multi-Target Tracking

2009 ◽  
Author(s):  
Fanbin Meng ◽  
Yanling Hao ◽  
Weidong Zhou ◽  
Feng Sun
Keyword(s):  
Target Tracking ◽  
Gaussian Mixture ◽  
Finite Sets ◽  
Probability Hypothesis Density ◽  
Probability Hypothesis Density Filter ◽  
Multi Target Tracking ◽  
Density Filter ◽  
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.

scholarly journals Time-Matching Random Finite Set-Based Filter for Radar Multi-Target Tracking

Sensors ◽  
2018 ◽  
Vol 18 (12) ◽  
pp. 4416 ◽  
Author(s):  
Defu Jiang ◽  
Ming Liu ◽  
Yiyue Gao ◽  
Yang Gao ◽  
Wei Fu ◽  
...  
Keyword(s):  
Target Tracking ◽  
Gaussian Mixture ◽  
Bayesian Filtering ◽  
Probability Hypothesis Density ◽  
Random Finite Set ◽  
Bayesian Formulation ◽  
Multi Target Tracking ◽  
Finite Set ◽  
Density Filter ◽  
Bernoulli Filter

The random finite set (RFS) approach provides an elegant Bayesian formulation of the multi-target tracking (MTT) problem without the requirement of explicit data association. In order to improve the performance of the RFS-based filter in radar MTT applications, this paper proposes a time-matching Bayesian filtering framework to deal with the problem caused by the diversity of target sampling times. Based on this framework, we develop a time-matching joint generalized labeled multi-Bernoulli filter and a time-matching probability hypothesis density filter. Simulations are performed by their Gaussian mixture implementations. The results show that the proposed approach can improve the accuracy of target state estimation, as well as the robustness.

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