Cubature Particle Filter and Its Application

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Cubature Particle Filter and Its Application

更新时间：2025-07-09

- Cubature Particle Filter and Its Application
- Vol. 45, Issue 8, Pages: 13-17(2011)
- 作者机构：
  
  1. 西安交通大学电子与信息工程学院,西安,710049
  2. 西安工业大学计算机科学与工程学院,西安,710032
- 作者简介：
- 基金信息：
- DOI：
  CLC： TP391
- Online First：10 August 2011，
  
  Published：2011
- 稿件说明：
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Cubature Particle Filter and Its Application[J]. 2011, 45(8): 13-17.
DOI：

Cubature Particle Filter and Its Application[J]. 2011, 45(8): 13-17. DOI：

摘要

针对使用现有粒子滤波算法对非线性/非高斯离散时间系统的状态估计精度较低的问题

提出了一种新的粒子滤波算法——容积粒子滤波(CPF)算法. 新算法使用容积数值积分原则直接计算非线性随机函数的均值和方差

产生粒子滤波算法的建议性密度函数

获得所需要的带权粒子

进而通过计算粒子均值

获得系统状态的最小均方误差估计. CPF算法由于产生粒子时使用了最新的测量信息

因而提高了对系统状态后验概率的逼近程度.仿真实验结果表明

CPF算法的估计误差约是标准粒子滤波算法和扩展粒子滤波算法误差的1/5和1/3

是无味粒子滤波(UPF)算法的估计误差的1/2

且运行时间只有UPF算法的1/3.

Abstract

A new particle filter named cubature particle filter(CPF)is proposed to improve the low state estimation accuracy of existing particle filters for nonlinear/non-Gaussian discrete time systems. The CPF directly uses the cubature rule based numerical integration method to calculate the mean and covariance

to generate the proposal density function for the particle filter

and to obtain the required particles with weights. Then the minimum square error state estimation is obtained based on these particles and weights. The particles generated using the CPF algorithm involves the use of the latest measurements so that the approximation to the system posterior density is improved. Simulation results show that the estimation error of the CPF algorithm is about one-fifth of that of the generic particle filter and one-third of that of the extended particle filter

respectively

and half of that of unscented particle filter(UPF)

while the run time of CPF is only one third of that of UPF.

关键词

Keywords

references

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引入模糊推理与强跟踪滤波技术的变结构多模型估计. 西安交通大学学报,2011,45(6):27-33.

观测域求积分卡尔曼滤波的机载无源定位算法. 西安交通大学学报,2011,45(5):137-142.

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