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

作　　者：牟忠凯[1] 隋立芬[2] 张清华[2] 甘雨[2] 

机构地区：[1]北京信息技术研究所,北京市8001信箱21号100094 [2]信息工程大学测绘学院,郑州市陇海中路66号450052

出　　处：《武汉大学学报（信息科学版）》2011年第9期1073-1076,共4页Geomatics and Information Science of Wuhan University

基　　金：国家自然科学基金资助项目(40974010)

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。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.

关 键 词：EKF 非线性 预测滤波 模型误差 线性化 

分 类 号：P207.1[天文地球—测绘科学与技术]