Gaussian Diffracted Particle Filter

您当前的位置：

首页 >

文章列表页 >

Gaussian Diffracted Particle Filter

更新时间：2025-07-09

- Gaussian Diffracted Particle Filter
- Vol. 44, Issue 6, Pages: 72-77(2010)
- 作者机构：
  
  第二炮兵工程学院测控系,西安,710025
- 作者简介：
- 基金信息：
- DOI：
  CLC： TP391
- Online First：10 June 2010，
  
  Published：2010
- 稿件说明：
移动端阅览
Gaussian Diffracted Particle Filter[J]. 2010, 44(6): 72-77.
DOI：

Gaussian Diffracted Particle Filter[J]. 2010, 44(6): 72-77. DOI：

摘要

针对基于高斯滤波的重要性采样方法运算量的明显增加主要集中在使用高斯滤波生成更好的重要性密度函数的问题

提出了一种新的高斯衍生粒子滤波算法(GDPF).该算法将一种类似光子衍射的粒子衍生重要性采样方法与现有的高斯辅助粒子滤波算法(GAPF)相结合

通过粒子的扩张与收缩

在保证不减少参与状态估计的粒子数的条件下减少更新粒子数

根据粒子权值大小自适应地调整衍生粒子数

能很好地缓解精度与运算量之间的矛盾

抑制粒子退化等问题. 对衍生粒子进行理论分析

证明了其与高斯采样粒子的等效性. 仿真结果表明

当选取了相同的参与状态估计的粒子数时

所提算法保持了与原算法相当的估计精度

同时运算量大大降低.

Abstract

The particle filter combined with Gaussian filter can restrict particle degeneracy with a secondary result that the new particle filter has a high calculation cost. In order to reduce the expensive calculation cost in the Gaussian aided particle filter(GAPF)

a Gaussian diffracted particle filter(GDPF)is proposed by introducing a light-diffracting-like particle diffracting sampling method into the current GAPF. The proposed method predicts fewer particles

and keeps more particles to be re-sampled from each Gaussian importance density function

so that the overall particles in the estimation of the system state are maintained the same by extending and contracting of particles. The number of particles is also adjusted according to particle weights. Therefore the calculation cost in GDPF is significantly reduced when the accuracy is required the same as the GAPF method

and the sample degeneracy problem is successfully improved. Theoretical analysis indicates that the efficiencies of both GDPF and GAPF are the same. The results of Monte Carlo simulations with the same number of particles in state estimation show that the improved particle filter can preserve the same accuracy of estimation while computation burden is greatly reduced.

关键词

Keywords

references

武元新. 对偶四元数导航算法与非线性高斯滤波研究[D]. 长沙: 国防科技大学机电工程与自动化学院, 2005.

DE FREITAS J F G. Sequential Monte Carlo methods to train neural network models [J]. Neural Computation, 2000, 12(4): 955-993.

VAN DER MERWE R, DE FREITAS N, DOUCET A, et al. The unscented particle filter [R]. Cambridge, UK: Cambridge Univ., 2000.

袁泽剑, 郑南宁, 贾新春. 高斯-厄米特粒子滤波器[J]. 电子学报, 2003, 31(7): 970-973.

YUAN Zejian, ZHENG Nanning, JIA Xinchun. The Gauss-Hermite Particle Filter [J]. Acta Electronica Sinica, 2003, 31(7): 970-973.

李良群, 姬红兵, 罗军辉. 迭代扩展卡尔曼粒子滤波器[J]. 西安电子科技大学学报, 2007, 34(2): 233-238.

LI Liangqun, JI Hongbing, LUO Junhui. Iterated extended Kalman particle filtering [J]. Journal of Xidian University, 2007, 34(2): 233-238.

巫春玲, 韩崇昭. 求积分卡尔曼粒子滤波算法[J]. 西安交通大学学报, 2009, 43(2): 25-28.

WU Chunling, HAN Chongzhao. Quadrature Kalman particle filter [J]. Journal of Xi'an Jiaotong University, 2009, 43(2): 25-28.

胡昌华, 张琪, 乔玉坤. 强跟踪粒子滤波算法及其在故障预报中的应用[J]. 自动化学报, 2008, 34(12): 1522-1528.

HU Changhua, ZHANG Qi, QIAO Yukun. A strong tracking particle filter with application to fault perdition [J]. Acta Electronica Sinica, 2008, 34(12): 1522-1528.

李甫, 齐飞, 石光明, 等. 一种补偿的扩展KALMAN粒子滤波 [J]. 系统仿真学报, 2009, 21(15): 4752-4758.

LI Fu, QI Fei, SHI Guangming,et al. Compensated extended Kalman particle filter [J]. Journal of System Simulation, 2009, 43(2): 25-28.

石勇, 韩崇昭. 二阶中心差分粒子滤波算法 [J]. 西安交通大学学报, 2008,42(4):409-413.

SHI Yong, HAN Chongzhao. Particle filter using second-order central difference [J]. Journal of Xi'an Jiaotong University, 2008, 42(4):409-413.

熊剑, 刘建业, 赖际舟, 等. 基于二阶插值滤波的粒子滤波改进算法研究 [J]. 控制与决策, 2009, 24(6): 907-910.

XIONG Jian, LIU Jianye, LAI Jizhou, et al. Improved particle filtering algorithm based on 2-order interpolation filtering[J]. Control and Decision, 2009, 24(6): 907-910.

Views

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

Improved Particle Filter Algorithm Based on Threshold De-Noising

Particle Filter Using Second-Order Central Difference

Quadrature Kalman Particle Filter

Pedestrian Tracking Using Directed Scene Motion Pattern and Particle Filter

Effects of Expansion Angle Crater Film Hole on the Film Cooling Characteristics of a Double-Wall Structure

Related Author

王向华 1

覃征 1

杨新宇 2

栗江涛 2

石勇

韩崇昭

巫春玲

屈鉴铭

Related Institution

Department of Computer Science and Technology, Tsinghua University

School of Electronics and Information Engineering, Xi'an Jiaotong University

School of Computer Science and Technology, Xidian University

⁰