A CNN-Based Type Recognition Method for Outliers of BDS Satellite Clock Bias

HUANG Bohua; LI Xirui; XIAO Changfu; HE Ruofeng; HAN Chongyan; GUO Zhiliang

doi:10.13203/j.whugis20190422

Volume 46 Issue 6

Jun. 2021

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Geomatics and Information Science of Wuhan University > 2021 > 46(6): 947-956. > DOI: 10.13203/j.whugis20190422

HUANG Bohua, LI Xirui, XIAO Changfu, HE Ruofeng, HAN Chongyan, GUO Zhiliang. A CNN-Based Type Recognition Method for Outliers of BDS Satellite Clock Bias[J]. Geomatics and Information Science of Wuhan University, 2021, 46(6): 947-956. DOI: 10.13203/j.whugis20190422

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A CNN-Based Type Recognition Method for Outliers of BDS Satellite Clock Bias

1.
63780 Troops, Sanya 572000, China
2.
Shanghai Astronomical Observatory, Chinese Academy of Sciences, Shanghai 200000, China
3.
College of Electronic Science, National University of Defense Technology, Changsha 410073, China

Funds:

The National Natural Science Foundation of China U1731130

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Author Bio:
HUANG Bohua, master, engineer, specializes in satellite clock data processing. E-mail: 15799034054@163.com
Corresponding author:
LI Xirui, master, senior engineer. E-mail: xrli@shao.ac.cn
Received Date: November 13, 2019
Published Date: June 04, 2021

Graphical Abstract

Abstract

Abstract

Objectives There are many different types of outliers in the satellite clock bias of BeiDou satellite navigation system(BDS), and these outliers directly affect the prediction quality of clock bias and the reliability of performance analysis. The preprocessing of clock bias is the precondition for the prediction and analysis of clock bias, and the recognition of outliers is the key to the preprocessing.
Methods This paper proposes a convolutional neural network(CNN)-based recognition method for outliers of BDS satellite clock bias. Firstly, a strategy is proposed to identify outliers by graphical feature differences of clock bias. Based on the principle of image recognition, outliers with different graphic features are classified to make the judgment of outliers more consistent with human cognitive habits. Then, a CNN-based recognition method for BDS clock bias is designed. Finally, by optimizing the model parameters, the accuracy of model recognition is improved continuously.
Results By using the BDS satellite clock data publicly provided by the International GNSS Service (IGS) data center, a training set and two test sets containing both phase data and frequency data are made. Under the same experimental conditions, the accuracy of phase model and frequency model in these two test sets are more than 99.6%.
Conclusions The proposed method can accurately identify the types of outliers and improve the quality of the pretreatment of clock bias.

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References (27)

References

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