一种新的卫星钟差Kalman滤波噪声协方差估计方法  

A new noise covariance matrix estimation method of Kalman filter for satellite clock errors

作　　者：林旭[1] 罗志才[1,2,3] 

机构地区：[1]武汉大学测绘学院,武汉430079 [2]武汉大学,地球空间环境与大地测量教育部重点实验室,武汉430079 [3]测绘遥感信息工程国家重点实验室,武汉430079

出　　处：《物理学报》2015年第8期14-19,共6页Acta Physica Sinica

基　　金：国家重点基础研究发展计划(批准号:2013CB733302);国家自然科学基金(批准号:41174062,41131067);中央高校基本科研业务费专项资金(批准号:2012214020206);地球空间环境与大地测量教育部重点实验室开放基金(批准号:12-02-09)资助的课题~~

摘　　要：采用Kalman滤波方法进行钟差参数计算和预报时,需确定Kalman滤波噪声协方差矩阵.针对这一问题,提出了一种新的卫星钟差Kalman滤波噪声协方差估计方法,通过建立新息的相关函数序列与未知的噪声参数间的线性函数模型,采用最小二乘法进行噪声参数估计.采用精密钟差数据进行钟差参数估计和预报分析,结果表明,该方法具有较好的收敛性,并与顾及随机噪声模型的开窗分类因子自适应抗差估计方法进行对比分析,验证了新方法的正确性和有效性.The satellite clock plays a key role in the global navigation satellite system （GNSS）. The accuracy of GNSS and its applications depend on the quality of the satellite clock. Therefore, precisely estimating and predicting the satellite clock is an important issue in the fields of GNSS and its application. As an optimal estimation algorithm, Kalman filter has been used to estimate and predict the satellite clock. However, in a conventional Kalman filter algorithm, the noise covariance matrices of satellite clock need to be predetermined, which restricts its further applications since the noise covariance matrices, especially the process noise covariance matrix, are usually unknown in the real cases. With inappropriate noise covariance matrices, the state estimation of conventional Kalman filter is suboptimal. To cope with this problem, a new noise covariance matrix estimation method of Kalman filter is proposed, and then we apply it to the problem of satellite clock estimation and prediction. Considering the fact that the process noise covariance matrix depends on the unknown noise parameters, the problem of estimating process noise covariance matrix can be solved by estimating the unknown noise parameters. First, the correlation between the Kalman innovations is used to establish a linear relationship with the unknown noise parameters. Then the unknown parameters can be estimated by least-squares estimation. Finally, the satellite clock can be estimated and predicted with the estimated noise parameters. In the new method, no prior information about the noise parameters is needed. Even with some extreme prior noise parameters, the new method can also work very well and has good convergence properties. For comparison, we conduct two experiments using the new method and the adaptively robust Kalman filter with classified adaptive factors based on opening windows separately, both results are consistent with each other very well, which verifies the correctness and effectiveness of this new method.

关 键 词：卫星钟差 KALMAN滤波 噪声估计 

分 类 号：O212.1[理学—概率论与数理统计]