A Novel Short-Medium Term Satellite Clock Error Prediction Algorithm Based on Modified Exponential Smoothing Method

Abstract

Clock error prediction is important for satellites while their clocks could not transfer time message with the stations in earth. It puts forth a novel short-medium term clock error prediction algorithm based on modified differential exponential smoothing (ES). Firstly, it introduces the basic double ES (DES) and triple ES (TES). As the weighted parameter in ES is fixed, leading to growing predicted errors, a dynamic weighted parameter based on a sliding window (SW) is put forward. And in order to improve the predicted precision, it brings in grey mode (GM) to learn the predicted errors of DES (TES) and combines the DES (TES) predicted results with the results of GM prediction from error learning. From examples' analysis, it could conclude that the short term predicted precisions of algorithms based on ES with GM error learning are less than 0.4ns, where GM error learning could better the performances slightly. And for the medium term, it could conclude that the fusion algorithm in DES (TES) with error learning in GM based on SW could reduce the predicted errors in 35.37% (66.34%) compared with DES (TES) alone. In medium term clock error prediction, the predicted precision of TES is worse than DES, which is roughly in the same level of GM.