| 引用本文: | 潘旺华,文援兰,朱俊,等.基于小波去噪半参数回归模型的卫星轨道测量数据预处理方法.[J].国防科技大学学报,2008,30(6):10-13.[点击复制] |
| PAN Wanghua,WEN Yuanlan,ZHU Jun,et al.Study on Satellite Orbit Tracking Data Reprocessing with Semi-parametric Regression Model and Based on the Wavelet De-noising Approach[J].Journal of National University of Defense Technology,2008,30(6):10-13[点击复制] |
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| 基于小波去噪半参数回归模型的卫星轨道测量数据预处理方法 |
| 潘旺华1,2, 文援兰1, 朱俊1, 廖瑛1 |
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(1.国防科技大学 航天与材料工程学院，湖南 长沙 410073;2.第二炮兵工程学院，陕西 西安 710025)
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| 摘要: |
| 由于卫星轨道测量数据中含有非线性误差，使用传统的最小二乘多项式拟合方法对其进行预处理必然会降低定轨精度。在半参数回归模型的基础上，应用小波阈值去噪算法估计并消除观测数据中存在的非线性误差，提出了基于小波去噪半参数回归模型的卫星轨道测量数据预处理方法，以提高数据预处理的精度。对某卫星USB跟踪数据应用该方法进行了仿真，仿真结果表明：该方法可以分离出观测数据中的白噪声和非线性误差，从而可以在观测数据中消除非线性误差的影响，提高数据预处理的精度。 |
| 关键词: 最小二乘 多项式拟合 半参数回归模型 小波阈值去噪 |
| DOI： |
| 投稿日期：2008-05-27 |
| 基金项目:国家自然科学基金资助项目(10573041) |
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| Study on Satellite Orbit Tracking Data Reprocessing with Semi-parametric Regression Model and Based on the Wavelet De-noising Approach |
| PAN Wanghua1,2, WEN Yuanlan1, ZHU Jun1, LIAO Ying1 |
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(1.College of Aerospace and Materials Engineering, National Univ. of Defense Technology, Changsha 410073, China;2.The Second Artillery Engineering Institute, Xi'an 710025, China)
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| Abstract: |
| Traditionally, the satellite orbit measurement data are reprocessed by least-square polynomial fit method. Because of the non-linear errors in the satellite tracking, the accuracy of data reprocessing is reduced. The current approach, based on the semi-parametric regression model, holds that the observations can be de-noising via wavelet threshold, and the nonlinear errors can be estimated and removed from the observations. The data reprocessing method with semi-parametric regression model based on the wavelet de-nosing approach is proposed to improve the accuracy of data reprocessing. Finally, the simulation of the reprocessing of a united S-band(USB) satellite orbit measurement data showed that the method could separate the white noise and nonlinear errors, and greatly improve the accuracy of data reprocessing. |
| Keywords: least square method polynomial fit semi-parametric regression model de-noising via wavelet threshold |
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