一种卡尔曼滤波与粒子滤波相结合的非线性滤波算法

doi:10.3969/j.issn.0372-2112.2013.01.026

电子学报 ›› 2013, Vol. 41 ›› Issue (1): 148-152.DOI: 10.3969/j.issn.0372-2112.2013.01.026

一种卡尔曼滤波与粒子滤波相结合的非线性滤波算法

夏楠¹, 邱天爽¹, 李景春², 李书芳³

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

收稿日期:2012-02-21 修回日期:2012-04-28 出版日期:2013-01-25
- 作者简介:
- 夏楠 男.1983年5月出生,辽宁大连人.2006年于大连交通大学信息工程专业获工学学士学位,现为大连理工大学电子信息与电气工程学部硕博连读生,主要研究方向为通信信号处理等. 邱天爽 男.1954年8月出生,江苏海门人.博士,大连理工大学电子信息与电气工程学部教授、博士生导师.主要从事信号信息处理方面的教学与研究工作.在国内外学术期刊与会议发表论文约180篇,曾获国家教育部科学技术二等奖等多项科技奖励. E-mail:qiutsh@dlut.edu.cn李景春男.1966年出生,河北宁晋人.博士,教授级高级工程师,国家无线电监测中心副主任兼总工程师,主要研究方向为无线电监测理论与应用等.李书芳女.1963年出生.博士,北京邮电大学信息与通信学院教授、博士生导师,主要研究方向为无线通信中的射频理论与技术和电磁兼容等.
- 基金资助:
- 国家自然科学基金 (No.61139001,No.61172108)

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

XIA Nan¹, QIU Tian-shuang¹, LI Jing-chun², LI Shu-fang³

1. Faculty of Electronic Information and Electrical Engineering, Dalian University of Technology, Dalian, Liaoning 116024, China;
2. State Radio Monitoring Center, Beijing 100037, China;
3. School of Information and Communication Engineering, Beijing University of Posts and Telecommunications, Beijing 100876, China

Received:2012-02-21 Revised:2012-04-28 Online:2013-01-25 Published:2013-01-25
- Supported by:
- National Natural Science Foundation of China (No.61139001, No.61172108)

摘要/Abstract

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.

关键词: 非线性滤波, 粒子滤波, 卡尔曼滤波, 克拉美劳下界, 计算复杂度

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.

Key words: nonlinear filtering, particle filter, Kalman filter, Cramer-Rao bound, computation complexity

中图分类号:

TN911.72

夏楠, 邱天爽, 李景春, 等. 一种卡尔曼滤波与粒子滤波相结合的非线性滤波算法[J]. 电子学报, 2013, 41(1): 148-152.

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.

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一种卡尔曼滤波与粒子滤波相结合的非线性滤波算法

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

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