scholarly journals Dynamic Partitioned Sampling For Tracking With Discriminative Features

2009 ◽  
Author(s):  
Stefan Duffner ◽  
Jean-Marc Odobez ◽  
Elisa Ricci
Keyword(s):  
Partitioned Sampling

2D Articulated Pose Tracking Using Particle Filter with Partitioned Sampling and Model Constraints

2009 ◽  
Vol 58 (2) ◽  
pp. 109-124 ◽  
Author(s):  
Chenguang Liu ◽  
Peng Liu ◽  
Jiafeng Liu ◽  
Jianhua Huang ◽  
Xianglong Tang
Keyword(s):  
Particle Filter ◽  
Pose Tracking ◽  
Partitioned Sampling

scholarly journals Adaptive Sample-Size Unscented Particle Filter with Partitioned Sampling for Three-Dimensional High-Maneuvering Target Tracking

2019 ◽  
Vol 9 (20) ◽  
pp. 4278 ◽  
Author(s):  
Qi Deng ◽  
Gang Chen ◽  
Huaxiang Lu
Keyword(s):  
Particle Filter ◽  
Sample Size ◽  
Target Tracking ◽  
Dimensional Space ◽  
Three Dimensional ◽  
Maneuvering Target Tracking ◽  
Time Step ◽  
Maneuvering Target ◽  
Unscented Particle Filter ◽  
Partitioned Sampling

High-maneuvering target tracking is a focused application area in radar positioning and military defense systems, especially in three-dimensional space. However, using a traditional motion model and techniques expanded from general two-dimensional maneuvering target tracking may be inaccurate and impractical in some mission-critical systems. This paper proposes an adaptive sample-size unscented particle filter with partitioned sampling (PS-AUPF), which is used to track a three-dimensional, high-maneuvering target, combined with the CS-jerk model. In PS-AUPF, the partitioned sampling is introduced to improve the resampling and predicting process by decomposing motion space. At the same time, the adaptive sample size strategy is used to adjust the sample size adaptively in the tracking process, according to the initial parameters and the estimated state variance of each time step. Finally, the effectiveness of this method is validated by simulations, in which the sample size of each algorithm is set to the minimum required for the optimal accuracy, thus ensuring the reliability of the tracking results. The results have shown that the proposed PS-AUPF, with higher accuracy and lower computational complexity, performs better than other existing tracking methods in three-dimensional high-maneuvering target tracking scenarios.

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