Opto-electric target tracking algorithm based on local feature selection and particle filter optimization

2018 ◽  
Vol 30 (22) ◽  
pp. e4670 ◽  
Author(s):  
Liangqing Peng ◽  
Yanpeng Wu ◽  
Lei Huang
Keyword(s):  
Feature Selection ◽  
Particle Filter ◽  
Target Tracking ◽  
Tracking Algorithm ◽  
Local Feature

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.

Target Tracking Algorithm Based on Kalman Filter and Particle Filter

2011 ◽  
Vol 26 (3) ◽  
pp. 384-389 ◽  
Author(s):  
杜超 DU Chao ◽  
刘伟宁 LIU Wei-ning ◽  
刘恋 LIU Lian
Keyword(s):  
Kalman Filter ◽  
Particle Filter ◽  
Target Tracking ◽  
Tracking Algorithm
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