Computer Engineering and Applications ›› 2010, Vol. 46 ›› Issue (19): 132-134.DOI: 10.3778/j.issn.1002-8331.2010.19.038

• 数据库、信号与信息处理 • Previous Articles     Next Articles

Method of improving central difference Kalman filter

YANG Hong1,2,LI Ya-an1,LI Guo-hui1,2,YUAN Run-ping3   

  1. 1.College of Marin,Northwestern Polytechnical University,Xi’an 710072,China
    2.School of Electronic and Engineering,Xi’an University of Post and Telecommunications,Xi’an 710061,China
    3.School of Electronics and Information Engineering,Xi’an Jiaotong University,Xi’an 710049,China
  • Received:2010-01-28 Revised:2010-04-20 Online:2010-07-01 Published:2010-07-01
  • Contact: YANG Hong

一种改进中心差分卡尔曼滤波方法

杨宏1,2,李亚安1,李国辉1,2,袁润平3   

  1. 1.西北工业大学航海学院,西安710072
    2.西安邮电学院电子工程学院,西安710061
    3.西安交通大学电子与信息工程学院,西安710049
  • 通讯作者: 杨宏

Abstract: In order to improve tracking estimation accuracy of existing Central Difference Kalman Filter(CDKF),a new iterative central difference kalman filter is proposed.In this paper,iterative filtering theory is introduced into the extended kalman filter algorithm,and observation information is reused.Taking the classic non-linear and non-gaussian model for example,several simulation experiments are given by using the algorithm such as extended kalman filter(EKF),Central Difference Kalman
Filter,and iterative central difference kalman filter.In comparison with the tracking performance and root mean square error,iterative central difference kalman filter(ICDKF) algorithm not only has no need to calculate Jacobian matrix,but also has a higher estimation accuracy.

摘要: 针对中心差分卡尔曼滤波(CDKF)跟踪时估计精度较低这一不足,提出了一种基于迭代测量更新的中心差分卡尔曼滤波(ICDKF)方法。本文将迭代滤波理论引入到中心差分卡尔曼滤波算法中,重复利用观测信息,采用经典的非线性非高斯模型进行仿真实验,给出了该算法与扩展卡尔曼滤波(EKF)、中心差分卡尔曼滤波(CDKF)的仿真结果,并分析了其跟踪性能和均方根误差。实验结果表明,迭代中心差分卡尔曼滤波(ICDKF)算法不仅具有无需计算Jacobian矩阵的优点,而且具有更高的估计精度。

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