An Efficient Multi-Target Tracking Algorithm Using Gaussian Mixture Probability Hypothesis Density Filter

2018 ◽  
Author(s):  
Li Gao ◽  
Huanqing Zhang ◽  
Ying Wang
Keyword(s):  
Target Tracking ◽  
Gaussian Mixture ◽  
Tracking Algorithm ◽  
Probability Hypothesis Density ◽  
Probability Hypothesis Density Filter ◽  
Multi Target Tracking ◽  
Density Filter

scholarly journals Adaptive Target Birth Intensity for ET-PHD Filter

2018 ◽  
Vol 176 ◽  
pp. 03010
Author(s):  
Lu Miao ◽  
Xin-xi Feng ◽  
Luo-jia Chi
Keyword(s):  
Target Tracking ◽  
Gaussian Mixture ◽  
Tracking Algorithm ◽  
Tracking Problem ◽  
Probability Hypothesis Density ◽  
Probability Hypothesis Density Filter ◽  
Target Probability ◽  
Extended Target ◽  
Density Filter ◽  
Simulation Results

An adaptive tracking algorithm based on Extended target Probability Hypothesis Density (ETPHD) filter is proposed for extended target tracking problem with priori unknown target birth intensity.The algorithm is implemented by gaussian mixture, where the target birth intensity is generated by measurement-driven, and the persistent and the newborn targets intensity are respectively predicted and updated. The simulation results show that the proposed algorithm improves the performance of the probability hypothesis density filter in the extended target tracking.

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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