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 sensitive to noise which may affect final monitoring results significantly. In order to suppress the nuclear signal noise, a sparse representation method, which is based on the sparse representation of signals and a matching pursuit algorithm, has been proposed for denoising. Time–frequency matching “atoms” have been selected for building an over-complete library by training atoms matching with the characteristics of nuclear signals regardless of the noise. The best time–frequency matching atoms have been extracted by sparsely representing the noisy signals with an Orthogonal Matching Pursuit (OMP) algorithm and the library. The residual ratio threshold has been chosen as a stopping criterion in the OMP algorithm for avoiding the influence of improper selection of iterations on denoising results. At the end, the pulse matching the atom extracted by each iteration has been optimized by performing effective sparse representation on the original noiseless nuclear signal component in noisy nuclear signals. The proposed method has been used to denoise the simulated and measured signals and has been compared with the nuclear denoising result using 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.