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

Qiang Liu,Yongshun Zhang,Xihong Chen,Denghua Hu,Zan Liu,Chenlong Li

doi:10.1155/2018/7486925

Qiang Liu, Yongshun Zhang + Show 4 more

Open Access

https://doi.org/10.1155/2018/7486925

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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.

Highlights

High precision time synchronization is of vital importance for the operation of satellites, which will influence the precision of satellites’ navigation, locating, and timing, and for distributed weapon systems, like the distributed netted radar system or the multistation radar system, etc., which will determine the precision of tracking, guiding, and locating directly
For the medium term, it could conclude that the fusion algorithm in double ES (DES) (TES) with error learning in grey mode (GM) based on sliding window (SW) could reduce the predicted errors in 35.37% (66.34%) compared with DES (TES) alone
We have mainly presented a novel fusion algorithm of DES (TES) in sliding window based on predicted error learning in GM for short-medium term clock error prediction

Summary

Introduction

High precision time synchronization is of vital importance for the operation of satellites, which will influence the precision of satellites’ navigation, locating, and timing, and for distributed weapon systems, like the distributed netted radar system or the multistation radar system, etc., which will determine the precision of tracking, guiding, and locating directly. Aiming at the short-medium term clock error prediction, there are many studies which had been done, like grey model, quadratic polynomial model, LS-SVM algorithm, ARMA algorithm, functional network, etc. As the break of clock messages will influence real-time precise point positioning (PPP), some methods, like optimal arc length identifying, different polynomial order predicting and empirical model composed of a sixth-order harmonic function, etc., were brought forth, which improved the performances of PPP [6,7,8]. Aiming at the application of ES in short-medium term satellite clock error prediction, we modify ES algorithm by a dynamic α based on a sliding window (SW). The MES algorithm’s applications in short-medium term clock error prediction are presented .

ES and Modified ES

ES learning

Examples and Analysis

Findings

Conclusion