scholarly journals Arbitrary clutter extended target probability hypothesis density filter

2021 ◽  
Author(s):  
Xinglin Shen ◽  
Luping Zhang ◽  
Moufa Hu ◽  
Shanzhu Xiao ◽  
Huamin Tao
Keyword(s):  
Probability Hypothesis Density ◽  
Probability Hypothesis Density Filter ◽  
Target Probability ◽  
Extended Target ◽  
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 Two Measurement Set Partitioning Algorithms for the Extended Target Probability Hypothesis Density Filter

Sensors ◽  
2019 ◽  
Vol 19 (12) ◽  
pp. 2665 ◽  
Author(s):  
Yulan Han ◽  
Chongzhao Han
Keyword(s):  
Mahalanobis Distance ◽  
Nearest Neighbors ◽  
Set Partitioning ◽  
Measurement Information ◽  
Probability Hypothesis Density ◽  
Dbscan Algorithm ◽  
Target Probability ◽  
Extended Target ◽  
Density Filter ◽  
Partitioning Algorithms

The extended target probability hypothesis density (ET-PHD) filter cannot work well if the density of measurements varies from target to target, which is based on the measurement set partitioning algorithms employing the Mahalanobis distance between measurements. To tackle the problem, two measurement set partitioning approaches, the shared nearest neighbors similarity partitioning (SNNSP) and SNN density partitioning (SNNDP), are proposed in this paper. In SNNSP, the shared nearest neighbors (SNN) similarity, which incorporates the neighboring measurement information, is introduced to DP instead of the Mahalanobis distance between measurements. Furthermore, the SNNDP is developed by combining the DBSCAN algorithm with the SNN similarity together to enhance the reliability of partitions. Simulation results show that the ET-PHD filters based on the two proposed partitioning algorithms can achieve better tracking performance with less computation than the compared algorithms.

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