| 一种新的北斗卫星钟差预处理方法 |
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| 引用本文： | 王旭,柴洪洲,种洋,李金生. 一种新的北斗卫星钟差预处理方法[J]. 武汉大学学报(信息科学版), 2022, 47(11): 1840-1846. DOI: 10.13203/j.whugis20200232 |
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| 作者姓名： | 王旭 柴洪洲 种洋 李金生 |
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| 作者单位： | 1.辽宁科技学院资源与土木工程学院, 辽宁 本溪, 117004 |
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| 基金项目： | 国家自然科学基金41574010国家自然科学基金41604013辽宁省教育厅高等学校基本科研项目lkyms202208 |
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| 摘 要： | 为了提高卫星钟差预报的精度，针对钟差数据中量级较小的误差，提出了一种基于中位数的小波阈值法钟差数据预处理策略。首先，利用小波阈值方法将钟差数据进行分解，得到分解后的高频系数和低频系数。然后，利用中位数法处理各层影响阈值设置的高频系数，通过处理后的高频系数计算阈值，从而提高小波阈值法剔除小异常值的能力。最后，用北斗二号卫星钟差数据进行了验证，结果表明，利用所提方法处理后的钟差数据建模，小波神经网络（wavelet neural network，WNN）模型预报的精度提高约14.1%，预报稳定性提高约19.7%。该方法可以有效剔除钟差历史观测序列中量级较小的误差，改善钟差数据质量，从而提高模型钟差预报的精度。
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| 关 键 词： | 卫星钟差 中位数 小波阈值法 小波神经网络 预报 |
| 收稿时间： | 2020-05-16 |
A New Data Preprocessing Method for BeiDou Satellite Clock Bias |
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| Affiliation: | 1.School of Resources and Civil Engineering, Liaoning Institute of Science and Technology, Benxi 117004, China2.Institute of Surveying and Mapping, Information Engineering University, Zhengzhou 450001, China3.Academy of Military Sciences, Beijing 100091, China4.Institute of Surveying and Mapping Engineering, Liaoning Vocational College of Ecological Engineering, Shenyang 110101, China |
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| Abstract: | Objectives In order to find a high accuracy method for satellite clock bias prediction, a preprocessing strategy for wavelet threshold method based on the median absolute deviation(MAD) is proposed to preprocess the small magnitude error of satellite clock bias data. Methods Firstly, the wavelet threshold method is used to decompose the SCB data to obtain the decomposed high frequency coefficient and low frequency coefficient.Then the MAD method is used to deal with the high frequency coefficient of each layer affecting the threshold setting, and the processed high frequency coefficient is used to calculate the threshold, so as to improve the ability of eliminating small outliers by the wavelet threshold method. Finally, the clock bias data of BeiDou-2 satellite are used to verify. Results The experimental results show that after modeling the clock bias data processed by the proposed method, the prediction accuracy of wavelet neural network(WNN) model is improved by about 14.1% and the prediction stability is improved by about 19.7%. Conclusions This method can effectively eliminate the small error in the historical observation sequence of clock bias, improve the quality of clock bias data and the effect of model clock bias prediction. |
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