A Nonlinear Filtering Algorithm Combining the Kalman Filter and the Particle Filter

XIA Nan; QIU Tian-shuang; LI Jing-chun; LI Shu-fang

doi:10.3969/j.issn.0372-2112.2013.01.026

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A Nonlinear Filtering Algorithm Combining the Kalman Filter and the Particle Filter

更新时间：2025-07-16

- A Nonlinear Filtering Algorithm Combining the Kalman Filter and the Particle Filter
- Acta Electronica Sinica Vol. 41, Issue 1, Pages: 148-152(2013)
- 作者机构：
  
  1. 大连理工大学电子信息与电气工程学部,辽宁,大连,116024
  2. 国家无线电监测中心,北京,100037
  3. 北京邮电大学信息与通信工程学院,北京,100876
  4. 大连理工大学电子信息与电气工程学部辽宁大连,116024
  5. 国家无线电监测中心北京,100037
  6. 北京邮电大学信息与通信工程学院北京,100876
- 作者简介：
- 基金信息：
  
  National Natural Science Foundation of China (No.61139001, No.61172108)
- DOI：10.3969/j.issn.0372-2112.2013.01.026
  CLC： TN911.72
- Published：2013
- 稿件说明：
移动端阅览
XIA Nan, QIU Tian-shuang, LI Jing-chun, et al. A Nonlinear Filtering Algorithm Combining the Kalman Filter and the Particle Filter[J]. Acta Electronica Sinica, 2013, 41(1): 148-152.
DOI：

XIA Nan, QIU Tian-shuang, LI Jing-chun, et al. A Nonlinear Filtering Algorithm Combining the Kalman Filter and the Particle Filter[J]. Acta Electronica Sinica, 2013, 41(1): 148-152. DOI： 10.3969/j.issn.0372-2112.2013.01.026.

摘要

提出一种基于卡尔曼滤波与粒子滤波的非线性滤波算法.这种方法对于状态变量服从线性变化而观测方程为非线性的动态系统模型具有显著的效果.首先使用粒子滤波对状态变量进行初估计

然后对估计结果进行卡尔曼滤波

另外推导出该系统模型下状态变量估计误差的克拉美劳下界.通过计算复杂度分析及仿真实验验证

表明新方法与标准粒子滤波算法复杂度相当

但参数估计精度要高于标准粒子滤波以及扩展卡尔曼滤波算法

估计误差甚至要低于系统模型的克拉美劳下界.

Abstract

A nonlinear filtering algorithm is proposed based on the Kalman filter and the particle filter.The method can provide significant performance for dynamic nonlinear system which is consist of linear state equation and nonlinear measurement equation.Firstly

the particle filter is utilized for initial estimation of the state variables

and then the Kalman filter is performed.The Cramer-Rao Bound is derived for the nonlinear model.Computation complexity analysis and numerical simulations demonstrate that the proposed algorithm has the same complexity as the standard particle filter

but the estimation accuracy is higher than the standard particle filter and the extended Kalman filter.The estimation error is even lower than the Cramer-Rao Bound of the system model.

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