Bridge dynamic deflection is an important indicator of structure safety detection. Ground-based microwave interferometry is widely used in bridge dynamic deflection monitoring because it has the advantages of noncontact measurement and high precision. However, due to the influences of various factors, there are many noises in the obtained dynamic deflection of bridges obtained by ground-based microwave interferometry. To reduce the impacts of noise for bridge dynamic deflection obtained with ground-based microwave interferometry, this paper proposes a morphology filter-assisted extreme-point symmetric mode decomposition (MF-ESMD) for the signal denoising of bridge dynamic deflection obtained by ground-based microwave interferometry. First, the original bridge dynamic deflection obtained with ground-based microwave interferometry was decomposed to obtain a series of intrinsic mode functions (IMFs) with the ESMD method. Second, the noise-dominant IMFs were removed according to Spearman’s rho algorithm, and the other decomposed IMFs were reconstructed as a new signal. Finally, the residual noises in the reconstructed signal were further eliminated using the morphological filter method. The results of both the simulated and on-site experiments showed that the proposed MF-ESMD method had a powerful signal denoising ability.

Ground-based synthetic aperture radar (GB-SAR) has a wide range of applications in bridge health detection by monitoring dynamic deflection data. However, the collected dynamic deflection signals are easily subjected to interference by noises during GB-SAR monitoring due to ground motion and complex traffic factors. It is also difficult to accurately eliminate the influence of noises by using the traditional modal decomposition method. Therefore, we propose a cyclically shifted extreme-point symmetric mode decomposition (CS-ESMD)-based progressive denoising approach, which can accurately identify high/low-frequency noise information from dynamic deflection signals through a progressive process. First, CS-ESMD is used to construct virtual multi-channel signals for the following progressive denoising process. Second, ESMD is performed on multi-channel dynamic deflection data to separate useful and high-frequency noise information. Finally, the low-frequency noises and the residual highfrequency noises are further identified and removed by second-order blind identification (SOBI) and the fast Fourier transform (FFT) method. Through simulation and practical experiments, we show that the accuracy of the progressive denoising method can be increased by 37.2% compared with traditional methods, which shows its effectiveness in improving the precision of GB-SAR dynamic deflection signals.

Ground-based microwave interferometry has been used extensively for dynamic deflection measurements of bridges. The Imaging by Interferometric Survey of Structures (IBIS-S) instrument is a system based on ground-based microwave interferometry, which consists of radar sensors for transmitting and receiving microwave and corresponding auxiliary units. To suppress the noise of the dynamic deflection of urban bridges obtained by ground-based microwave interferometry, by integrating singular value decomposition (SVD) and extremepoint symmetric mode decomposition (ESMD), a novel ESMD-SVD denoising method is proposed in this paper. First, the original bridge signal obtained by the IBIS-S sensor was decomposed to obtain a series of intrinsic mode functions (IMFs) and a signal trend term by ESMD. Second, the signal trend term was extracted and the IMFs were reconstructed, which removed the effect of the signal trend term on the singular value selection in SVD. SVD was used to eliminate the random noises and other noises in the reconstructed signal. Finally, the denoised signal obtained by SVD was superimposed on the trend term to obtain the final denoised signal. The results of both simulated and on-site experiments showed that the proposed ESMD-SVD method had a powerful signal denoising ability.