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Fusion-based Satellite Clock Bias Prediction Considering Characteristics and Fitted Residue

Published online by Cambridge University Press:  05 February 2018

Jicang Lu ,
Chao Zhang ,
Yong Zheng  and
Ruopu Wang
Jicang Lu*
Affiliation:
(Zhengzhou Information Science and Technology Institute, China)
Chao Zhang
Affiliation:
(Zhengzhou Information Science and Technology Institute, China)
Yong Zheng
Affiliation:
(Zhengzhou Information Science and Technology Institute, China)
Ruopu Wang
Affiliation:
(Zhengzhou Information Science and Technology Institute, China)
*
(E-mail: [email protected])
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Abstract

As Satellite Clock Bias (SCB) prediction may be affected by various factors such as periodic items, sampling length, and stochastic items, a fusion-based prediction method is proposed by considering characteristics of SCB and fitted residue. On this basis, an instance algorithm is presented by fusing four typical prediction models. First, we use Empirical Mode Decomposition (EMD) to pre-process and decompose the SCB series into multiple components with various characteristics. Then, we analyse the fitting performance of each model for different components and prediction length, namely short-, mid- and long-term prediction, and select models with the best performance. Next, we analyse fitted residue of the reconstructed SCB, and select the model with the best fitting results. Finally, we fuse the multiple selected models for SCB prediction. The method is tested using Global Positioning System (GPS) precise clock products provided by the International Global Navigation Satellite System Service (IGS). Experimental results show that, compared with single prediction models and existing combination models, the proposed fusion-based prediction method improves accuracy and stability. In particular, the proposed method is more stable and has better performance for mid- and long-term prediction.

Keywords

Satellite clock bias (SCB) predictionEMD (Empirical Mode Decomposition)ResidueMultiple model fusion
Type
Research Article
Information
The Journal of Navigation , Volume 71 , Issue 4 , July 2018 , pp. 955 - 970
Copyright
Copyright © The Royal Institute of Navigation 2018 

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