Non-Degeneracy Particle Filtering Method Research for Particle Diversity Preserving

WU Hao; SUN Xiao-yan; GUO Yu-tang; LIU Lu-lu; SHEN Jing

doi:10.3969/j.issn.0372-2112.2016.07.031

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Non-Degeneracy Particle Filtering Method Research for Particle Diversity Preserving

更新时间：2025-07-16

- Non-Degeneracy Particle Filtering Method Research for Particle Diversity Preserving
- Acta Electronica Sinica Vol. 44, Issue 7, Pages: 1734-1741(2016)
- 作者机构：
  
  1. 中国矿业大学信息与电气工程学院,江苏,徐州,221008
  2. 合肥师范学院计算机学院,安徽,合肥,230601
  3. 中国矿业大学信息与电气工程学院,江苏,徐州,221008
  4. 合肥师范学院计算机学院,安徽,合肥,230601
- 作者简介：
- 基金信息：
  
  National Natural Science Foundation of China (No.61301062, No.61503116, No.61375067);Fundamental Research Funds for the Central Universities (No.2012QNA58);Natural Science Foundation for Colleges and Universities in Anhui Province (No.KJ2013A217);Quality Engineering Improvement Project of Anhui Province (No.2013zy058);Foundation for Excellent Young Talent in univerities of Education Department of Anhui Province (No.SQRL129ZD)
- DOI：10.3969/j.issn.0372-2112.2016.07.031
  CLC： TP18
- Published：2016
- 稿件说明：
移动端阅览
WU Hao, SUN Xiao-yan, GUO Yu-tang, et al. Non-Degeneracy Particle Filtering Method Research for Particle Diversity Preserving[J]. Acta Electronica Sinica, 2016, 44(7): 1734-1741.
DOI：

WU Hao, SUN Xiao-yan, GUO Yu-tang, et al. Non-Degeneracy Particle Filtering Method Research for Particle Diversity Preserving[J]. Acta Electronica Sinica, 2016, 44(7): 1734-1741. DOI： 10.3969/j.issn.0372-2112.2016.07.031.

摘要

针对现有粒子滤波算法中的粒子退化问题以及重采样所引起的粒子多样性减弱问题

将自适应遗传算法与粒子滤波结合设计一种新的非退化粒子滤波算法.该算法通过对粒子使用遗传算子操作以保证粒子的多样性和有效性

根据粒子在前一时刻计算出来的先验信息自适应地实时调节当前时刻的遗传操作概率

有效增加了粒子对系统状态变化的适应性.实验结果表明

该算法可有效提高非线性系统状态的估计精度

尤其在系统状态发生突变的时候

可以得到较好的估计精度.

Abstract

To cope with the degeneracy in the existing particle filter algorithm and the diversity weakening caused by re-sampling

a new non-degeneracy algorithm is proposed in this paper by incorporating adaptive genetic algorithm into particle filter.By using genetic operators to generate new particles

the algorithm can adjust the current probability of genetic manipulation adaptively based on the previously calculated information so that the diversity and effectiveness of the particle can be ensured.It effectively improves the adaptability of particle to the changes of the system state.Experimental results show that this algorithm can effectively improve the estimation accuracy of the nonlinear system state.In particular

the algorithm can guarantee good estimation accuracy when the system state changes abruptly.

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