WMAD-based GNSS satellite clock error data preprocessing method

Abstract

The invention aims to provide a WMAD-based GNSS satellite clock error data preprocessing method. The method effectively solves the problem that a classical gross error detection method cannot effectively eliminate errors with smaller magnitude in clock errors. The method specifically comprises the steps of firstly carrying out primary difference calculation on the clock error of a GNSS satellite to obtain primary difference data of the clock error; performing wavelet decomposition on the clock difference primary difference data to obtain a decomposed wavelet coefficient; then eliminating a larger wavelet coefficient by using an MAD method; calculating the threshold value of each layer, and carrying out thresholding treatment; and finally, performing wavelet reconstruction to obtain preprocessed clock difference primary difference data. The invention provides an effective method for satellite clock error data preprocessing, and the quality of the clock error data is improved.

Description

technical field [0001] The invention relates to a GNSS satellite clock error data processing method, which belongs to the technical field of geodesy and navigation surveying and mapping. Background technique [0002] GNSS satellite-borne atomic clocks will undergo operations such as frequency modulation and clock switching during long-term operation, and will also be affected by various uncertain factors. The clock error data inevitably contains some gross errors. If the clock error data containing gross errors is used Data analysis and modeling forecast will greatly affect the quality of data analysis and modeling forecast. Therefore, scholars at home and abroad have done a lot of research on the elimination of gross errors in satellite clock errors. For the processing of gross errors in clock error data, commonly used methods include the Median Absolute Deviation (MAD) method, robust estimation method, and Bayesian method, etc. Among them, the MAD method has the advantag...

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Problems solved by technology

[0003] The gross errors contained in the clock error data can be effectively eliminated by the above method, but in the actual clock error processing process, it is found that there are often errors (outliers) with small magnitudes in the clock error data after the gross errors have been removed, while the classical It is difficult to effectively eliminate these outliers by the gross error detection method, and these outliers will also affect the quality of clock error data analysis and modeling forecast, so before using the clock error data, the amount of clock error data Reasonable and effective preprocessing is a very necessary step

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