Anti-interference Mechanism of ICA Blind Source Separation Combined with Wavelet De-noising
Abstract: In the issues of
signal de-noising, utilize K-SVD and other classical dictionary learning
algorithm for sparse decomposition and reconstruction of signal, which cannot
effectively eliminate influence ofnoise. The method suggested by this paper
makes some improvement to the classical dictionary learning. Firstly, utilize
K-SVD algorithm to make the dictionary learning; then, utilize the method of
non-linear least squares to fit each atom in the dictionary and get the revised
dictionary; finally, utilize the method of Particle Swarm Optimization to solve
the spare representation of signal and get the reconstructed signal at last. It
is proved through the experience that the de-noising effect of this paper is
obvious superior to the conventional dictionary learning algorithm and is close
to the effect of wavelet analytical approach.
Author: Liu Sheng, Zhou
Shuanghong, Li Bing, Zhang Lanyong
Journal Code: jptkomputergg160110
