一种融合 UKF 和EKF 的粒子滤波状态估计算法

doi:10.3969/j.issn.1001-506X.2013.07.4

Journal of Systems Engineering and Electronics

一种融合 UKF 和EKF 的粒子滤波状态估计算法

于洪波¹，王国宏¹，孙芸¹，曹倩²

1．海军航空工程学院信息融合技术研究所，山东烟台264001;
2．海军航空工程学院图书馆，山东烟台264001

收稿日期:2012-01-17 修回日期:2013-01-15 出版日期:2013-07-22 发布日期:2013-05-15

A particle filtering algorithm of state estimation on fusion of UKF and EKF

YU Hong-bo¹, WANG Guo-hong¹, Sun Yun¹, Cao Qian²

1.Institute of Information Fusion, Naval Aeronautical and Astronautical University,Yantai264001,China;
2. Library of Naval Aeronautical and Astronautical University,Yantai264001,China

Received:2012-01-17 Revised:2013-01-15 Online:2013-07-22 Published:2013-05-15

摘要/Abstract

摘要：

在扩展卡尔曼滤波算法(Extended Kalman Filter，EKF)和不敏卡尔曼滤波算法(Unscented Kalman Filter，UKF)的基础上，提出一种基于融合的粒子滤波算法(Fusion based particle filter, FPF)。该算法首先利用EKF 与UKF 分别预测粒子状态，然后通过融合算法得到粒子的重要性建议分布，实现粒子状态更新。因为充分利用了量测信息，因而能有效提高状态估计精度。仿真中通过实例将该算法与已有的粒子滤波算法(Particle filter algorithm,PF)进行比较，结果表明该算法各方面性能都有较大改进。

Abstract:

Based on the Extended Kalman Filter (EKF) algorithm and Unscented KalmanFilter (UKF) algorithm, a new fusion based particle filtering algorithm (FPF) is presented in this
paper, in which the importance density function is generated by means of a fusion algorithm. Toderive the importance density of samples, the state of each particle is predicted separatelyaccording to EKF and UKF. An application example is given to draw a comparison between thisnew algorithm and the existing particle filter algorithm(PF). The results show that new algorithmoutperforms the existing particle filter algorithm in all aspects.

于洪波，王国宏，孙芸，曹倩. 一种融合 UKF 和EKF 的粒子滤波状态估计算法[J]. Journal of Systems Engineering and Electronics, doi: 10.3969/j.issn.1001-506X.2013.07.4.

YU Hong-bo, WANG Guo-hong, Sun Yun, Cao Qian. A particle filtering algorithm of state estimation on fusion of UKF and EKF[J]. Journal of Systems Engineering and Electronics, doi: 10.3969/j.issn.1001-506X.2013.07.4.

一种融合 UKF 和EKF 的粒子滤波状态估计算法

A particle filtering algorithm of state estimation on fusion of UKF and EKF

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