-
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。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.
-
-
![]()
图 1 3颗卫星的钟差数据及其对应的一次差分数据
Figure 1. Clock Bias Data of 3 Satellites and Their Corresponding Primary Difference Data
![]()
图 2 小波阈值法和本文方法预处理效果图
Figure 2. Effect of Preprocessing with Wavelet Threshold and the Proposed Method
![]()
图 3 3种模型在24 h预报时长下所有卫星预报的精度
Figure 3. Prediction Accuracy of All Satellites Under 24 h Predicting Time Lengths by 3 Models
表 1 小波阈值法和本文方法预处理后的统计值
Table 1 Statistics of Preprocess Results of Wavelet Threshold and the Proposed Methods
卫星钟 小波阈值法 本文方法 SNR/dB RMSE/ns SNR/dB RMSE/ns C01 110.451 0.043 111.465 0.029 C08 101.805 0.025 102.684 0.017 C01 113.349 0.032 114.647 0.021 
下载: 导出CSV
表 2 WNN、W-WNN、new-WNN 3种模型预报效果统计值/ns
Table 2 Statistics of Prediction Results of WNN, W-WNN and new-WNN Models/ns
卫星轨道
类型WNN模型 W-WNN模型 new-WNN模型 RMSE Range RMSE Range RMSE Range GEO卫星 2.017 2.124 1.900 1.971 1.744 1.621 IGSO卫星 1.775 2.407 1.759 2.334 1.549 2.053 MEO卫星 1.468 1.334 1.430 1.361 1.194 1.133 平均值 1.733 1.979 1.685 1.919 1.488 1.589
下载: 导出CSV
表 3 new-WNN和预报产品两种模型预报结果RMSE和Range统计值/ns
Table 3 Statistics of Prediction RMSE and Range Results of new-WNN Model and Prediction Products/ns
模型
轨道类型钟差预报产品 new-WNN模型 RMSE Range RMSE Range GEO卫星 8.944 15.256 1.744 1.621 IGSO卫星 15.398 28.231 1.549 2.053 MEO卫星 2.452 3.411 1.194 1.133 平均值 9.469 16.714 1.483 1.589
下载: 导出CSV
-
[1] Mosavi M R, Shafiee F. Narrowband Interference Suppression for GPS Navigation Using Neural Networks[J]. GPS Solutions, 2016, 20(3): 341-351
[2] 王宇谱, 吕志平, 周海涛, 等. 基于修正钟差一次差分数据的卫星钟差预报[J]. 大地测量与地球动力学, 2016, 36(12): 1073-1077 https://www.cnki.com.cn/Article/CJFDTOTAL-DKXB201612009.htm Wang Yupu, Lü Zhiping, Zhou Haitao, et al. Satellite Clock Bias Prediction Based on Modified Single Difference Data of Clock Bias[J]. Journal of Geodesy and Geodynamics, 2016, 36(12): 1073-1077 https://www.cnki.com.cn/Article/CJFDTOTAL-DKXB201612009.htm
[3] Li X. Comparing the Kalman Filter with a Monte Carlo-Based Artificial Neural Network in the INS/GPS Vector Gravimetric System[J]. Journal of Geodesy, 2009, 83(9): 797-804
[4] Wang Y P, Lu Z P, Qu Y Y, et al. Improving Prediction Performance of GPS Satellite Clock Bias Based on Wavelet Neural Network[J]. GPS Solutions, 2017, 21(2): 523-534 doi: 10.1007/s10291-016-0543-z
[5] 周佩元, 杜兰, 路余, 等. 多星定轨条件下北斗卫星钟差的周期性变化[J]. 测绘学报, 2015, 44(12): 1299-1306 https://www.cnki.com.cn/Article/CJFDTOTAL-CHXB201512002.htm Zhou Peiyuan, Du Lan, Lu Yu, et al. Periodic Variations of BeiDou Satellite Clock Offsets Derived from Multisatellite Orbit Determination[J]. Acta Geodaetica et Cartographica Sinica, 2015, 44(12): 1299-1306 https://www.cnki.com.cn/Article/CJFDTOTAL-CHXB201512002.htm
[6] 张倩倩, 韩松辉, 杜兰, 等. 星地时间同步钟差异常处理的Batesian方法[J]. 武汉大学学报·信息科学版, 2016, 41(6): 772-777 doi: 10.13203/j.whugis20140666 Zhang Qianqian, Han Songhui, Du Lan, et al. Bayesian Methods for Outliers Detection and Estimation in Clock Offset Measurements of Satellit-Ground Time Transfer[J]. Geomatics and Information Science of Wuhan University, 2016, 41(6): 772-777 doi: 10.13203/j.whugis20140666
[7] 王宇谱. GNSS星载原子钟性能分析与卫星钟差建模预报研究[D]. 郑州: 信息工程大学, 2017 Wang Yupu. Research on Modeling and Prediction of the Satellite Clock Bias and Performance Evaluation of GNSS Satellite Clocks[D]. Zhengzhou: Information and Engineering University, 2017
[8] 吴静. 利用中位数的GPS卫星钟跳探测方法[J]. 测绘科学, 2015, 40(6): 36-41 https://www.cnki.com.cn/Article/CJFDTOTAL-CHKD201506008.htm Wu Jing. Study on Detection of GPS Clock Jump Using Median Absolute Deviation[J]. Science of Surveying and Mapping, 2015, 40(6): 36-41 https://www.cnki.com.cn/Article/CJFDTOTAL-CHKD201506008.htm
[9] 袁开明, 舒乃秋, 孙云莲, 等. 基于阈值寻优法的小波阈值去噪分析[J]. 武汉大学学报·信息科学版, 2015, 48(1): 74-79 Yuan Kaiming, Shu Naiqiu, Sun Yunlian, et al. Wavelet Denoising Based on Threshold Optimiza-tion Method[J]. Geomatics and Information Science of Wuhan University, 2015, 48(1): 74-79
[10] 李文涛, 颜雄, 夏磊, 等. BDS卫星钟差半参数平差模型异常数据探测与处理[J]. 测绘学报, 2020, 49(1): 56-63 https://www.cnki.com.cn/Article/CJFDTOTAL-CHXB202001006.htm Li Wentao, Yan Xiong, Xia Lei, et al. Abnormal Data Detection and Process by Using BDS Satellite Offset Semiparametric Adjustment Model[J]. Acta Geodaetica et Cartographica Sinica, 2020, 49(1): 56-63 https://www.cnki.com.cn/Article/CJFDTOTAL-CHXB202001006.htm
[11] 王旭, 王昶. 一种基于熵权法的小波去噪复合评价指标[J]. 大地测量与地球动力学, 2018, 38(7): 698-702 https://www.cnki.com.cn/Article/CJFDTOTAL-DKXB201807008.htm Wang Xu, Wang Chang. A Kind of Wavelet De-noising Composite Evaluation Index Based on Entropy Method[J]. Journal of Geodesy and Geodynamics, 2018, 38(7): 698-702 https://www.cnki.com.cn/Article/CJFDTOTAL-DKXB201807008.htm
[12] 王旭, 王昶. 一种改进小波阈值去噪方法的研究[J]. 大地测量与地球动力学, 2017, 37(10): 1038-1041 https://www.cnki.com.cn/Article/CJFDTOTAL-DKXB201710010.htm Wang Xu, Wang Chang. An Improved Wavelet Threshold Denoising Method Research[J]. Journal of Geodesy and Geodynamics, 2017, 37(10): 1038-1041 https://www.cnki.com.cn/Article/CJFDTOTAL-DKXB201710010.htm
[13] 郭承军, 藤云龙. 基于小波分析和神经网络的卫星钟差预报性能分析[J]. 天文学报, 2010, 51(4): 395-399 https://www.cnki.com.cn/Article/CJFDTOTAL-TWXB201004010.htm Guo Chenjun, Teng Yunlong. Performance Analysis of Satellite Clock Bias Based on Wavelet Analysis and Neural Network[J]. Acta Astronomica Sinica, 2010, 51(4): 395-399 https://www.cnki.com.cn/Article/CJFDTOTAL-TWXB201004010.htm
[14] 王昶, 随心, 王旭. 基于分层阈值去噪法处理建筑物变形监测数据[J]. 辽宁工程技术大学学报(自然科学版), 2015, 34(11): 1271-1274 https://www.cnki.com.cn/Article/CJFDTOTAL-FXKY201511012.htm Wang Chang, Sui Xin, Wang Xu. Processing of Monitoring Data of Building Deformation Based on Wavelet Threshold Denoising[J]. Journal of Liaoning Technical University(Natural Science), 2015, 34(11): 1271-1274 https://www.cnki.com.cn/Article/CJFDTOTAL-FXKY201511012.htm
[15] 王旭, 刘文生, 王昶. 一种改进小波阈值去噪方法的研究[J]. 大地测量与地球动力学, 2011, 36(5): 1038-1041 https://www.cnki.com.cn/Article/CJFDTOTAL-DKXB201710010.htm Wang Xu, Liu Wensheng, Wang Chang. An Improved Wavelet Threshold Denoising Method Research[J]. Science of Surveying and Mapping, 2011, 36(5): 1038-1041 https://www.cnki.com.cn/Article/CJFDTOTAL-DKXB201710010.htm
[16] 王宇谱, 吕志平, 崔阳, 等. 利用遗传小波神经网络预报导航卫星钟差[J]. 武汉大学学报·信息科学版, 2014, 39(7): 809-814 http://ch.whu.edu.cn/article/id/3025 Wang Yupu, Lü Zhiping, Cui Yang, et al. Predicting Navigation Satellite Clock Bias Using a Genetic Wavelet Neural Network[J]. Geomatics and Information Science of Wuhan University, 2014, 39(7): 809-814 http://ch.whu.edu.cn/article/id/3025
[17] 王旭, 柴洪洲, 王昶. 卫星钟差预报的T-S模糊神经网络法[J]. 测绘学报, 2020, 49(5): 580-587 https://www.cnki.com.cn/Article/CJFDTOTAL-CHXB202005005.htm Wang Xu, Chai Hongzhou, Wang Chang. T-S Fuzzy Neural Network to Predict Satellite Clock Bias[J]. Acta Geodaetica et Cartographica Sinica, 2020, 49(5) : 580-587 https://www.cnki.com.cn/Article/CJFDTOTAL-CHXB202005005.htm
[18] 王威, 王宇谱, 王彬, 等. BDS卫星精密钟差性能综合分析[J]. 导航定位学报, 2020, 8(1): 20-26 Wang Wei, Wang Yupu, Wang Bin, et al. Accuracy Evaluation of BDS Satellite Precise Clock Bias[J]. Journal of Navigation and Positioning, 2020, 8(1): 20-26
-
期刊类型引用(10)
1. 曾广泉，马韬，张孟希，戴妍，陈凯文，丁继辉，俞双恩，王中文. 基于无人机多光谱影像的不同施氮量水稻LAI反演方法研究. 江苏农业科学. 2024(20): 41-48 . 
百度学术
2. 高钰琪，许桂玲，冯跃华，王晓珂，任红军，由晓璇，韩志丽，李家乐. 基于冠层高光谱植被指数的水稻产量预测模型研究. 中国稻米. 2023(05): 38-44 . 百度学术
3. 彭晓伟，张爱军，王楠，赵丽，杨晓楠. 高光谱技术在土壤及适种作物的研究进展. 遥感信息. 2022(01): 32-39 . 百度学术
4. 王晓珂，刘婷婷，许桂玲，冯跃华，彭金凤，李杰，罗强鑫，韩志丽，卢苇，PHONENASAY Somsana. 基于冠层高光谱遥感的杂交水稻植被指数氮素营养诊断模型. 中国稻米. 2021(03): 21-29 . 百度学术
5. 王浩淼，宋苗语，李翔，扈朝阳，鲁任翔，王翔，马会勤. 无人机高光谱遥感监测葡萄长势与缺株定位. 园艺学报. 2021(08): 1626-1634 . 百度学术
6. 刘雅婷，龚龑，段博，方圣辉，彭漪. 多时相NDVI与丰度综合分析的油菜无人机遥感长势监测. 武汉大学学报(信息科学版). 2020(02): 265-272 . 百度学术
7. 陈晓凯，李粉玲，王玉娜，史博太，侯玉昊，常庆瑞. 无人机高光谱遥感估算冬小麦叶面积指数. 农业工程学报. 2020(22): 40-49 . 百度学术
8. 落莉莉，常庆瑞，武旭梅，杨景，李粉玲，王琦. 夏玉米叶片光合色素含量高光谱估算. 干旱地区农业研究. 2019(04): 178-183 . 百度学术
9. 张良培，刘蓉，杜博. 使用量子优化算法进行高光谱遥感影像处理综述. 武汉大学学报(信息科学版). 2018(12): 1811-1818 . 百度学术
10. 李亚妮，鲁蕾，刘勇. 基于PROSAIL模型的水稻田缨帽三角-叶面积指数模型及其应用. 应用生态学报. 2017(12): 3976-3984 . 百度学术
其他类型引用(17)
计量
- 文章访问数: 1371
- HTML全文浏览量: 284
- PDF下载量: 126
- 被引次数: 27
