文献类型：期刊文献

中文题名：一种强跟踪扩展卡尔曼滤波器的改进算法

英文题名：Improved Method of Strong Tracking Extended Kalman Filter

作者：范文兵[1];刘春风[1];张素贞[2]

机构：[1]郑州大学信息工程学院,郑州450052;[2]华东理工大学自动化研究所,上海200237

年份：2006

卷号：21

期号：1

起止页码：73

中文期刊名：控制与决策

外文期刊名：Control and Decision

收录：CSTPCD;;EI(收录号：2006129772995);Scopus;北大核心:【北大核心2004】；CSCD:【CSCD2011_2012】；

基金：国家863高科技计划项目(2002AA412120);江苏省科技攻关项目(BE2005035)

语种：中文

中文关键词：有限差分;强跟踪滤波;非线性系统;模型失配;状态估计

外文关键词：Finite-difference; Strong tracking filtering; Nonlinear system; Model mismatch; State estimation

摘要：针对模型不匹配卡尔曼的状态估计发散和应用范围限于连续系统问题,提出一种基于有限差分强跟踪滤波器(STFDEKF).在滤波计算中,引入强跟踪滤波因子修正滤波器的状态预协方差矩阵,滤波精度得以提高;滤波器应用有限差分方法计算滤波过程中非线性函数的偏导数,扩大了适用范围.几种卡尔曼滤波器经过仿真比较,STFDEKF应用于复杂非线性系统状态估计时,具有较高数值稳定性、强跟踪性和较宽应用范围.
A strong tracking finite-difference Kalman filter （STFDEKF） is presented to handle the divergence problem of state estimation of a nonlinear mismatched model and limited application scope. In filtering calculation, strong tracking factor is introduced to modify priori covariance matrix to improve the accuracy of the filter. The filter uses finite-difference method to calculate partial derivatives of nonlinear functions to enlarge its application scope. The comparison of several Kalman filters shows that the STFDEKF filter has high numerical stability, strong tracking and larger application scope and it can be applied to state estimation of complex nonlinear systems.