基于M估计卡尔曼滤波的GPS校频系统频差处理方法研究

M-Estimation Kalman Filter Based Frequency Deviation Processing for GPS Frequency Calibration

在采用GPS卫星信号驯服地面晶振方法设计的频率源中,GPS与晶振的频率差信号存在噪声干扰。利用标准卡尔曼滤波算法直接对频率差信号进行滤波处理,信号噪声中的野值会影响滤波精度。提出一种基于最小和函数估计卡尔曼滤波方法,通过该M估计的影响函数导出加权矩阵,判别频差信号是否为野值,进而对滤波新息进行修正。在某GPS校频系统中的应用表明,野值对滤波精度的影响得到有效抑制,系统输出频率准确度优于5.0×10^(-12)。

In frequency source designed by using GPS satellite signals to tame the ground crystal oscillator,there is interference noise in frequency deviation between GPS and crystal oscillator. If using Kalman filter to process the frequency deviation directly, outliers in the signal noise will influence the accuracy of filtering. In this paper, a Kalman filter algorithm based on minimum sum function estimation was proposed, which used the weighing matrix derived by influence function of M-estimation to judge whether the frequency deviation has outlier, thus to correct it for new filter information. The method has been used for some frequency calibration systems. The results show that the influence of outliers on the filtering accuracy can be effectively suppressed, and the output frequency accuracy is higher than 5. 0 × 10～（-12）.