A new sampling method in particle filter based on Pearson correlation coefficient

被引：275

作者：

Zhou, Haomiao ^{[1
]}

Deng, Zhihong ^{[1
]}

Xia, Yuanqing ^{[1
]}

Fu, Mengyin ^{[1
]}

机构：

[1] Beijing Inst Technol, Sch Automat, Beijing 100081, Peoples R China

来源：

NEUROCOMPUTING | 2016年 / 216卷

关键词：

Particle filter; Pearson correlation coefficient; Importance density;

D O I：

10.1016/j.neucom.2016.07.036

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Particle filters have been proven to be very effective for nonlinear/non-Gaussian systems. However, the great disadvantage of a particle filter is its particle degeneracy and sample impoverishment. An improved particle filter based on Pearson correlation coefficient (PPC) is proposed to reduce the disadvantage. The PPC is adopted to determine whether the particles are close to the true states. By resampling the particles in the prediction step, the new PF performs better than generic PF. Finally, some simulations are carried out to illustrate the effectiveness of the proposed filter. (C) 2016 Elsevier B.V. All rights reserved.

引用

页码：208 / 215

页数：8

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