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  

Study on Satellite Orbit Tracking Data Reprocessing with Semi-parametric Regression Model and Based on the Wavelet De-noising Approach

作　　者：潘旺华[1,2] 文援兰[1] 朱俊[1] 廖瑛[1] 

机构地区：[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

出　　处：《国防科技大学学报》2008年第6期10-13,共4页Journal of National University of Defense Technology

基　　金：国家自然科学基金资助项目(10573041)

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。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 semiparametric 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 reprccessing. 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.

关 键 词：最小二乘 多项式拟合 半参数回归模型 小波阅值去噪 

分 类 号：V412[航空宇航科学与技术—航空宇航推进理论与工程]