牟忠凯, 隋立芬, 张清华, 甘雨. 顾及线性化模型误差补偿的卡尔曼滤波算法[J]. 武汉大学学报 ( 信息科学版), 2011, 36(9): 1073-1076.
引用本文: 牟忠凯, 隋立芬, 张清华, 甘雨. 顾及线性化模型误差补偿的卡尔曼滤波算法[J]. 武汉大学学报 ( 信息科学版), 2011, 36(9): 1073-1076.
MOU Zhongkai, SUI Lifen, ZHANG Qinghua, GAN Yu. An Improved EKF Algorithm Considering Model Errors of Linearization[J]. Geomatics and Information Science of Wuhan University, 2011, 36(9): 1073-1076.
Citation: MOU Zhongkai, SUI Lifen, ZHANG Qinghua, GAN Yu. An Improved EKF Algorithm Considering Model Errors of Linearization[J]. Geomatics and Information Science of Wuhan University, 2011, 36(9): 1073-1076.

顾及线性化模型误差补偿的卡尔曼滤波算法

An Improved EKF Algorithm Considering Model Errors of Linearization

  • 摘要: 针对扩展卡尔曼滤波(EKF)线性化所产生的线性化模型误差问题,使用非线性预测滤波对线性化所引起的模型误差进行预测,并在标准EKF的解算过程中考虑到预测所得误差的统计特性,使模型更趋于真实情况。通过算例对改进算法的性能进行了验证。

     

    Abstract: Extended Kalman filtering(EKF) is an effective method for nonlinear problem.However,linearization error is introduced in the process of transforming the nonlinear problem to the linear one.For the linear model error problem,the nonlinear predictive filtering(NPF) is used to predict the linear model error in the nonlinear problem,which is caused by extended Kalman filtering(EKF).The statistic properties of predicted model errors is combined with the process noise in the standard EKF to make the model more exact.Lastly,the performance of NPEKF and EKF is compared by an simulation example.The results show the validity of the compensation algorithm of model errors.

     

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