文献类型：期刊文献

中文题名：基于Cauchy鲁棒函数的UKF改进算法

英文题名：Improved UKF Algorithm Based on Cauchy Robust Function

作者：高丽君[1];刘济[1]

机构：[1]华东理工大学信息科学与工程学院,上海200237

年份：2015

卷号：41

期号：2

起止页码：244

中文期刊名：华东理工大学学报（自然科学版）

外文期刊名：Journal of East China University of Science and Technology

收录：CSTPCD;;Scopus;北大核心:【北大核心2014】；CSCD:【CSCD2015_2016】；

基金：上海市自然科学基金(11ZR1409800)

语种：中文

中文关键词：噪声估计不准;不敏卡尔曼滤波;Cauchy鲁棒函数

外文关键词：inaccurate noise estimation; unscented Kalman filter（UKF） ; Cauchy robust function

摘要：对于大多数实际系统,其噪声统计特性未知,不敏卡尔曼滤波(unscented Kalman filter,UKF)算法对噪声信息不准的鲁棒性较差,导致滤波精度急剧下降,甚至滤波发散。借助鲁棒数据校正的思想,提出了一种基于Cauchy鲁棒函数的UKF改进算法。以UKF的测量先验值与其实际值的残差作为基准,采用联合权函数对噪声估计值进行实时修正,从而提高了UKF算法的精度。通过两个实例的仿真,验证该算法的有效性。
The statistics of noise are usually unknown in most actual systems. Moreover, it is known that the Unscented Kalman Filter （UKF） has poor robustness on the inaccurate noise information, which also makes the filter accuracy rapidly decrease, even diverge. By using the robust correction of data, this work proposes an improved UKF algorithm based on Cauehy Robust function. The noise estimation is adaptively corrected by the combining weighted function, which is calculated from the residual error between process measurement and its estimation. Thus, the accuracy of the UKF algorithm can be increased. Finally, two simulation examples are provided to show the effectiveness of the proposed algorithm.