A Noise Reduction Method for GB-RAR Bridge Monitoring Data Based on CEEMD-WTD and PCA

The ground-based real aperture radar (GB-RAR), with its non-contact, high-precision, continuous monitoring capabilities, is widely used in bridge safety. To reduce noise interference in GB-RAR monitoring, a denoising method based on complementary ensemble empirical mode decomposition (CEEMD), wavelet threshold denoising (WTD), and principal component analysis (PCA) was applied to the safety monitoring of the East Lake High-tech Bridge in Wuhan. The method involved CECEEMD of GB-RAR data, WTD for high-frequency noise Intrinsic Mode Function (IMF) components, and PCA for low-frequency IMF power spectrum matrices to remove coloured noise. PCA shows a symmetric balance between noise removal and signal retention. The experimental results show that the proposed denoising method ensures the integrity of the reconstructed signal by symmetrically processing the IMF of high and low frequencies and improves the signal-to-noise ratio (SNR) of the three piers to 8.30, 19.87 and 15.06, respectively, and the Root Mean Square Errors (RMSE) are 0.10 mm, 0.06 mm and 0.09 mm, respectively. Noise removal reduced uncertainty by 42.3%, 35.8%, and 33.1%, demonstrating the method’s effectiveness in enhancing deformation monitoring precision.