Particle filter-based target tracking algorithm with adaptive multi-feature fusion

2011 ◽  
Author(s):  
Mingming Wang ◽  
Weining Zhang ◽  
Yang Yang
Keyword(s):  
Particle Filter ◽  
Target Tracking ◽  
Feature Fusion ◽  
Tracking Algorithm

Robust tracking algorithm based on multi-feature fusion and particle filter

2010 ◽  
Vol 30 (3) ◽  
pp. 643-645 ◽  
Author(s):  
Wei ZENG ◽  
Gui-bin ZHU ◽  
Jie CHEN ◽  
Ding-ding TANG
Keyword(s):  
Particle Filter ◽  
Feature Fusion ◽  
Tracking Algorithm ◽  
Robust Tracking

Infrared Target Tracking Algorithm Based on Multiple Feature Fusion and Region of Interest Prediction

2019 ◽  
Vol 48 (7) ◽  
pp. 710004 ◽  
Author(s):  
刘辉 LIU Hui ◽  
何勇 HE Yong ◽  
何博侠 HE Bo-xia ◽  
刘志 LIU Zhi ◽  
顾士晨 GU Shi-chen
Keyword(s):  
Target Tracking ◽  
Feature Fusion ◽  
Region Of Interest ◽  
Tracking Algorithm ◽  
Multiple Feature ◽  
Multiple Feature Fusion ◽  
Interest Prediction

scholarly journals Research on the Particle Filter Single-Station Target Tracking Algorithm Based on Particle Number Optimization

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Lieping Zhang ◽  
Jinghua Nie ◽  
Shenglan Zhang ◽  
Yanlin Yu ◽  
Yong Liang ◽  
...  
Keyword(s):  
Particle Filter ◽  
Target Tracking ◽  
Real Time ◽  
Particle Number ◽  
Tracking Algorithm ◽  
Tracking Accuracy ◽  
Time Performance ◽  
Single Station ◽  
Tracking Model ◽  
Number Of Particles

Given that the tracking accuracy and real-time performance of the particle filter (PF) target tracking algorithm are greatly affected by the number of sampled particles, a PF target tracking algorithm based on particle number optimization under the single-station environment was proposed in this study. First, a single-station target tracking model was established, and the corresponding PF algorithm was designed. Next, a tracking simulation experiment was carried out on the PF target tracking algorithm under different numbers of particles with the root mean square error (RMSE) and filtering time as the evaluation indexes. On this basis, the optimal number of particles, which could meet the accuracy and real-time performance requirements, was determined and taken as the number of particles of the proposed algorithm. The MATLAB simulation results revealed that compared with the unscented Kalman filter (UKF), the single-station PF target tracking algorithm based on particle number optimization not only was of high tracking accuracy but also could meet the real-time performance requirement.

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