An Improved Particle Filter Based on Space Distribution and Time Series Analysis

YANG Wei-ming; ZHAO Mei-rong; HUANG Yin-guo; LI Han-chen

doi:10.3969/j.issn.0372-2112.2017.02.006

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An Improved Particle Filter Based on Space Distribution and Time Series Analysis

更新时间：2025-07-16

- An Improved Particle Filter Based on Space Distribution and Time Series Analysis
- Acta Electronica Sinica Vol. 45, Issue 2, Pages: 300-306(2017)
- 作者机构：
  
  1. 天津大学精密测试技术及仪器国家重点实验室,天津,300072
  2. 天津科技大学电子信息与自动化学院,天津,300222
  3. 天津大学精密测试技术及仪器国家重点实验室,天津,300072
  4. 天津科技大学电子信息与自动化学院,天津,300222
- 作者简介：
- 基金信息：
  
  Youth Fund of National Natural Science Foundation of China (No.61304246);Science and Technology Development Fund of Higher Education Institutions in Tianjin Municipality (No.20130707)
- DOI：10.3969/j.issn.0372-2112.2017.02.006
  CLC： TP391
- Published Online：25 February 2017，
  
  Published：2017
- 稿件说明：
移动端阅览
YANG Wei-ming, ZHAO Mei-rong, HUANG Yin-guo, et al. An Improved Particle Filter Based on Space Distribution and Time Series Analysis[J]. Acta Electronica Sinica, 2017, 45(2): 300-306.
DOI：

YANG Wei-ming, ZHAO Mei-rong, HUANG Yin-guo, et al. An Improved Particle Filter Based on Space Distribution and Time Series Analysis[J]. Acta Electronica Sinica, 2017, 45(2): 300-306. DOI： 10.3969/j.issn.0372-2112.2017.02.006.

摘要

针对粒子滤波存在的粒子贫化问题，提出了一种改进的重采样粒子滤波算法.在重采样步骤中基于采样粒子集的空间分布引入时间序列分析，选取相关度最高的粒子进行传递，避免了只关注采样粒子权值的传统重采样算法中仅复制大权值粒子而任意丢弃小权值粒子的缺陷，因此能够消弱粒子贫化现象，提高算法的估计精度.在理论上利用两样本Kolmogorov-Smirnov检验原理证明了改进算法重采样后的粒子集和采样前的粒子集来自同一总体.仿真结果表明，尤其是在初始采样粒子数目较小时，该算法在非线性系统状态估计中的精度优于传统的粒子滤波算法.

Abstract

In order to solve the problem of sample particles impoverishment

an improved resampling particle filter is presented.It is based on the space distribution and time series analysis.The most important particle that has higher temporal correlation between the particle's path and observation path in particle propagating is chosen.It can avoid the problem in the traditional resampling algorithm that only the particle's weights are considered

and the low weighed particles have the risk to be thrown away.Thus the problem of particles impoverishment is weakened and the estimate accuracy is improved.By the two-sample Kolmogorov-Smirnov Test

a proof is given that the particles that are resampled by the improved algorithm and the original particles belong to the same distribution.The proposed approach

verified by simulations

indicates that its accuracy is better than traditional methods for the nonlinear system state estimation

especially when the number of initial sampling particles is small.

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