Abstract
Ground-based synthetic aperture radar (GBSAR) technology, as a new measurement technology, has the advantages of noncontact measurements, high precision, and all-weather measurement capability, and it has been widely used for bridge dynamic deflection measurements. In order to reduce the influence of noise in dynamic deflection of bridges obtained using GBSAR, this article proposes a single-channel blind source separation signal (SCBSS) de-noising method to obtain the denoised dynamic deflection of bridges. First, the extreme-point symmetric mode decomposition (ESMD) method and the ensemble empirical mode decomposition (EEMD) method are used to decompose the obtained dynamic deflection—as the original observation signal—into a series of intrinsic mode functions (IMFs) and a residual R. Second, the Spearman's Rho of each IMF with the original observation signal is calculated to remove the dominant IMFs of high-frequency noise. Third, the remaining IMFs and R decomposed by ESMD and EEMD are reconstructed into two sets of new signals, which form a new virtual multichannel data with the original observation signal. Finally, blind source separation is performed on the new virtual multichannel signal to obtain separated signal components. The separate signal components are converted in the frequency domain using the fast Fourier transform algorithm, and the noise signal components are identified using a spectrum analysis, to achieve further removal of noise information. The results of both simulated and on-site experiments show that the SCBSS signal de-noising method has a powerful signal de-noising ability.
Highlights
I T PLAYS an important role for bridges in transportation networks
The ensemble empirical mode decomposition (EEMD) and extreme-point symmetric mode decomposition (ESMD) methods can reduce the impact of high-frequency noise by removing low-order IMFS, but there may still be low-frequency noise and some residual highfrequency noise in the remaining intrinsic mode functions (IMFs) components
A single EEMD or ESMD method cannot remove the influence of lowfrequency noise on the signal
Summary
I T PLAYS an important role for bridges in transportation networks. due to the combination of factors, such as service life, environment, overload, and geological activities, the load-carrying capacity of bridges is gradually decreasing [1]–[3]. The wavelet decomposition method uses the wavelet basis function to decompose the original single-channel signal into subcomponents of each frequency, and reconstruct the subcomponent signals separately to obtain multipath signals [30].The EMD method can adaptively decompose the signal into a series of IMFs, and select the appropriate IMFs and original data to form a multichannel signal [12], [31]. The EEMD and ESMD methods can adaptively decompose the signal into a series of IMFs from high frequency to low LIU et al.: SCBSS SIGNAL DE-NOISING METHOD OF INTEGRATING EEMD AND ESMD FOR DYNAMIC DEFLECTION OF BRIDGES frequency, which can reduce the influence of mode-mixing problem. The BSS method is used to separate the above three virtual multichannel signals to obtain the separated signal components, and extract useful information from the separated signal components to further remove low-frequency noise and some residual high-frequency noise
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