CAJ | 학술논문

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。
채용Kalman려파방법진행종차삼수계산화예보시，수학정Kalman려파조성협방차구진。침대저일문제，제출료일충신적위성종차Kalman려파조성협방차고계방법，통과건립신식적상관함수서렬여미지적조성삼수간적선성함수모형，채용최소이승법진행조성삼수고계。채용정밀종차수거진행종차삼수고계화예보분석，결과표명，해방법구유교호적수렴성，병여고급수궤조성모형적개창분류인자자괄응항차고계방법진행대비분석，험증료신방법적정학성화유효성。
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.