1. SCBSS Signal De-Noising Method of Integrating EEMD and ESMD for Dynamic Deflection of Bridges Using GBSAR
- Author
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Huang Yimeng, Xianglei Liu, and Hui Wang
- Subjects
Atmospheric Science ,010504 meteorology & atmospheric sciences ,Computer science ,Geophysics. Cosmic physics ,ground-based synthetic aperture radar (GBSAR) ,02 engineering and technology ,01 natural sciences ,Blind signal separation ,Signal ,Hilbert–Huang transform ,0202 electrical engineering, electronic engineering, information engineering ,single-channel blind source separation (SCBSS) ,Computers in Earth Sciences ,ensemble empirical mode decomposition (EEMD) ,TC1501-1800 ,0105 earth and related environmental sciences ,Noise measurement ,QC801-809 ,Noise (signal processing) ,Wavelet transform ,020206 networking & telecommunications ,Time–frequency analysis ,Ocean engineering ,Dynamic deflection ,Frequency domain ,signal de-noising ,extreme-point symmetric mode decomposition (ESMD) ,Algorithm - 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. more...
- Published
- 2021
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