Open AccessDenoising of MST RADAR Signal usingCWT and Overlapping Group Shrinkage
Author(s) -
G. Sreenivasulu P. Suresh Babu
Publication year - 2021
Publication title -
türk bilgisayar ve matematik eğitimi dergisi
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.218
H-Index - 3
ISSN - 1309-4653
DOI - 10.17762/turcomat.v12i5.1474
Subject(s) - wavelet , radar , noise reduction , computer science , norm (philosophy) , minification , algorithm , shrinkage , regular polygon , artificial intelligence , mathematics , mathematical optimization , geometry , telecommunications , machine learning , political science , law
Existing algorithmsare generally denouncing the existence of clusters with large amplitude coefficients. The L1 norm as well as other distinct models of sparsity does not attract a cluster tendency (group sparsity). In the light of a minimisation of convex cost work fusing the blended norm, this work introduces the technique "overlapping group shrinking." The groups are completely overlapping in order to abstain from blocking relics. A basic minimization calculation, in light of progressive replacement, is inferred. A straightforward strategy for setting the regularization boundary, in view of constricting the noise to a predefined level, is portrayed in detail by combining OGS with one of the most powerful mathematical tool wavelet transforms. In fact, the CWT coefficients are processed by OGS to produce a noise-free signal. The CWT coefficients are also processed.The proposed approach is represented on MST RADAR signals, the denoised signals delivered by CWT combined with OGS are liberated from noise.