抗差自适应Kalman滤波及其在GNSS导航中的应用  

Adaptive robust Kalman filtering and its application in GNSS

作　　者：邹敏[1] 王国栋[2] 刘超[1] 

机构地区：[1]安徽理工大学测绘学院,安徽淮南232001 [2]浙江有色测绘院,浙江绍兴312000

出　　处：《河北工程大学学报（自然科学版）》2016年第3期89-93,共5页Journal of Hebei University of Engineering:Natural Science Edition

基　　金：国家自然科学基金资助项目(41404004);安徽省自然科学基金资助项目(1408085QD72);安徽理工大学科研启动基金资助项目(11152)

摘　　要：针对Kalman滤波易受粗差影响而导致结果失真的问题,提出一种抗差自适应Kalman滤波方法,该方法结合自适应滤波与抗差Kalman滤波的优点,同时设计自适应因子和抗差因子,采用改进的两段Huber函数与2~3倍的观测噪声中误差来充当抗差因子与粗差判别标准。并对Kalman滤波和抗差自适应滤波（Adaptive Robust Kalman Filtering,ARKF）结果进行比较。车载实验结果表明,ARKF可以有效抵制观测异常对状态估值的影响,同时在系统先验信息不能精确给出的情况下,显著改善了滤波估值的稳定性和可用性。The Kalman filtering is easily affected by the gross error and it will cause larger distortion of the result. To overcome this problem,an adaptive robust Kalman filtering was proposed. It combines the advantages of adaptive Kalman filtering and robust Kalman filtering by using the adaptive factor and the robust factor. And the improved two segments Huber function was designed as the robust factor,the two to three times observation noise error was designed as the gross error determination standard respectively. Compared the result of Kalman filtering and adaptive robust Kalman filtering,the latter could effectively resist the influence of abnormal observation to the state estimation,and can improve the stability and availability of the filtering estimation significantly.

关 键 词：KALMAN滤波 粗差 抗差自适应Kalman滤波 GNSS 

分 类 号：P228[天文地球—大地测量学与测量工程]