AUTHOR=He San-Jun , Sun Na , Su Ling-Ling , Chen Bin , Zhao Xiu-Liang 

TITLE=Denoising Method of Nuclear Signal Based on Sparse Representation

JOURNAL=Frontiers in Energy Research

VOLUME=Volume 10 - 2022

YEAR=2022

URL=https://www.frontiersin.org/journals/energy-research/articles/10.3389/fenrg.2022.837823

DOI=10.3389/fenrg.2022.837823

ISSN=2296-598X

ABSTRACT=Nuclear signals are susceptible to noise interference which significantly affect final monitoring results. Aiming at the problem of nuclear signal noise suppression, a method of nuclear signal sparse representation denoising is proposed in this work. This method takes the sparse representation of signal and its matching pursuit algorithm as the critical point, constructs time-frequency matching atoms matched with the characteristics of nuclear signal regardless the noise, and finally builds an overcomplete atomic library. In the atomic library, the Orthogonal Matching Pursuit (OMP) algorithm is applied to sparsely represent the nucleus signal and extract the best time-frequency matching atom. At the same time, the OMP algorithm chooses the residual ratio threshold as stopping criterion and thus avoids the interference of iterations on denoising result. Finally, the optimal pulse matching atom extracted by each iteration can only perform effective sparse representation on the original noiseless kernel component in the noise-suppressed nuclear signal to achieve denoising. The proposed method is used to denoise the simulated and measured signal and compared with the nuclear denoising result based on traditional wavelet theory. The results show that the proposed method can accurately suppress the noise interference of nuclear signals, and the denoising effect is better than that of the traditional wavelet method.