Abstract: The present application relates to the field of satellite navigation systems, and discloses a real-time sliding ultrashort-term forecast model algorithm based on frequency data and phase data. The real-time sliding ultrashort-term forecast model algorithm based on frequency data and phase data includes: S1, converting clock error phase data into the frequency data; S2, processing the frequency data through a frequency anomaly detection function, and eliminating an abnormal value, where the frequency anomaly detection function determines whether the frequency data is an outlier according to a standard deviation and a threshold of the frequency data; and S3, performing a real-time sliding clock error forecast, and eliminating the abnormal value and updating a forecast epoch by using fitted phase data and a threshold range.

Abstract: The present application relates to the field of satellite navigation systems, and discloses a real-time sliding ultrashort-term forecast model algorithm based on frequency data and phase data. The real-time sliding ultrashort-term forecast model algorithm based on frequency data and phase data includes: S1, converting clock error phase data into the frequency data; S2, processing the frequency data through a frequency anomaly detection function, and eliminating an abnormal value, where the frequency anomaly detection function determines whether the frequency data is an outlier according to a standard deviation and a threshold of the frequency data; and S3, performing a real-time sliding clock error forecast, and eliminating the abnormal value and updating a forecast epoch by using fitted phase data and a threshold range.