Aiming at the challenges of noise suppression and deformation information extraction in the processing of global navigation satellite system (GNSS) monitoring data,the paper proposed a method for data processing and information extraction of GNSS railway slope deformation using a combination of Bayesian information criterion (BIC)-principal component analysis (PCA) and local mean decomposition (LMD) algorithms:considering determining the number of principal components in PCA,the BIC was introduced into PCA to establish a BIC-PCA model;and the BIC-PCA was used to analyze the deformation monitoring data to achieve noise suppression;then,the LMD algorithm was used to analyze the monitoring data after noise suppression,and the implicit deformation information such as periodic terms,trend terms and fluctuation terms were extracted;finally,a support vector regression (SVR) model was established to predict future deformation trends.Experimental results showed that the proposed method would have high prediction accuracy and strong noise robustness,the root mean square (RMS) error and average prediction error (APRE) of the prediction results could be 6.30 and 7.26,respectively,which are much smaller than those of BP and GM(1,1) methods.
global navigation satellite systemrailway slopedeformation predictiondata analysisnoise suppressionlocal mean decomposition (LMD)
万方数据
维普
