Particle filter tracking algorithm based on error ellipse resampling

被引：0
|
作者：
Xu C. [1 ,2 ]
Wang X. [1 ,2 ]
Duan S. [1 ,2 ]
Wan J. [1 ,2 ]
机构：
[1] School of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing
[2] Shunde Graduate School, University of Science and Technology Beijing, Foshan
来源：
Duan, Shihong (duansh@ustb.edu.cn) | 2020年 / Science Press卷 / 41期
关键词：
Cumulative error optimization; Error ellipse; Particle filter; Posterior Cramer-Rao lovoer bound (PCRLB); Resampling;
D O I：
10.19650/j.cnki.cjsi.J2006813
中图分类号：
学科分类号：
摘要：
Real-time and reliable navigation and localization is the key technology of wireless sensor networks. In practical application environment, the error model of the positioning system is generally nonlinear and non-Gaussian, and the traditional Kalman filtering algorithm cannot provide long time and high precision positioning service. In present research, particle filter can deal with complex system and measurement models, but it often faces the problems of particle degradation and impoverishment in practical applications. Aiming at this problem, a particle filter algorithm based on error ellipse resampling is proposed, which can be used for target localization tracking in wireless sensor networks. In order to improve the effective precision of particle filter algorithm in state estimation, establish the error ellipses with different confidence levels in the resampling process according to the particle error covariance matrix, stratifies the particles according to their geometric positions, then conducts screening and optimization of the particles with different stratification levels, and verifies the effectiveness of the proposed method in cumulative error optimization through comparing with that of computing the posterior Cramer-Rao lovoer bound (PCRLB). The experiment results show that the error elliptic resampling particle filter algorithm reaches the accuracy of 1.05 m, which can effectively improve the particle degradation and impoverishment issues. © 2020, Science Press. All right reserved.
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页码：76 / 84
页数：8
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