GNSS signal processing based on improved lifting wavelet transform with prior constraint

During deformation monitoring via GNSS (Global Navigation Satellite System), the initial GNSS signal typically comprises abundant interference information, and controlling the influences of the GNSS noises and extracting pure structural vibration information become the challenging issue. Therefore, an improved three-segment soft threshold function was proposed to control the influence of the noises, and it is the prerequisite for extracting useful vibration information. Meanwhile, prior information such as the known frequency or the vibration characteristics of the construction and the significant noises can help further to improve the efficiency of the lifting wavelet transform. Thus, the wavelet decomposition was applied toward the denoised signal to extract useful vibration information and significant noises based on the prior information. The improved algorithm was implemented and compared with the conventional lifting wavelet transform in the coordinate calculation of the GNSS monitoring point. Experimental results indicate that the improved lifting wavelet transform performed better than the conventional lifting wavelet transform in signal denoising, and the valid structural vibration information and significant noises can be simply identified based on the prior information constraint. This research can provide valuable references for GNSS data processing, dynamic deformation information extraction, and external load analysis.