Signal Denoising Method Based on Atom Curve Fitting Improved Dictionary Learning

Authors

Gao Bo, National Key Lab of Radar Signal Processing, Xidian Univ. , Xi’an 710071, China;
Wang Jun, National Key Lab of Radar Signal Processing, Xidian Univ. , Xi’an 710071, China;
Gege Zhang, National Key Lab of Radar Signal Processing, Xidian Univ. , Xi’an 710071, China;

Abstract

Abstract: In signal denoising problems, using K-SVD and other classic dictionary learning algorithm can not effectively eliminate the noise impact. The method made some amendments for classical dictionary learning by applying nonlinear least squares curve fitting and particle swarm optimization. K-SVD algorithm was used to train the dictionary. Nonlinear least-squares approach was used to fit every atom in the dictionary. Particle swarm optimization method was used to solve the sparse representation of the signal. The reconstructed signal was obtained. The experimental results show that, the denoising effects of the proposed method apparently has increased compared with K-SVD and RLS-DLA.

Recommended Citation

Bo, Gao; Jun, Wang; and Zhang, Gege (2020) "Signal Denoising Method Based on Atom Curve Fitting Improved Dictionary Learning," Journal of System Simulation: Vol. 27: Iss. 12, Article 9.
Available at: https://dc-china-simulation.researchcommons.org/journal/vol27/iss12/9

First Page

2935

Revised Date

2015-07-23

DOI Link

https://doi.org/

Last Page

2941

CLC

TN911

Recommended Citation

Gao Bo, Wang Jun, Zhang Gege. Signal Denoising Method Based on Atom Curve Fitting Improved Dictionary Learning[J]. Journal of System Simulation, 2015, 27(12): 2935-2941.