一种基于小波分析的卫星钟差数据粗差处理方法

A Preprocess Method for Gross Error Detection Based on Wavelet Analysis

针对传统MAD方法在探测钟差粗差方面存在的不足,提出一种基于小波分析的钟差数据粗差探测与处理方法。该方法利用小波变换的多尺度分析能力,将含有粗差的数据分解为低频小波系数和各层的高频小波系数,并在不同时间尺度下进行粗差探测和消除。使用CODE提供的BDS精密钟差数据进行实验,分析不同小波函数及不同分解尺度对预处理效果的影响,并与传统MAD方法比较,发现利用小波分解预处理后的数据在钟差预报方面有明显优势,预报精度平均提高10%。

Due to the deficiency of the traditional MAD method in detecting the gross clock error,we proposed a method of detecting and processing the gross error of clock data based on wavelet algorithm.By using the multi-scale analysis capability of wavelet transform,the data with gross errors are decomposed into the low-frequency wavelet coefficients and the high-frequency wavelet coefficients of each layer,and the gross errors are detected and eliminated at different time scales.Using the BDS clock bias data provided by CODE(Center for Orbit Determination in Europe),we analyze the performance of different wavelet functions and decomposition scale.Compared with the MAD method,we find that after preprocessing using wavelet analysis,the data has obvious advantages in clock prediction,and the average prediction precision increases by 10%.