TITLE:
Iterative Reweighted l1 Penalty Regression Approach for Line Spectral Estimation
AUTHORS:
Fei Ye, Xian Luo, Wanzhou Ye
KEYWORDS:
Line Spectral Estimation, Penalty Regression, Bayesian Lasso, Iterative Reweighted Approach
JOURNAL NAME:
Advances in Pure Mathematics,
Vol.8 No.2,
February
26,
2018
ABSTRACT: In this paper, we proposed an iterative reweighted l1penalty
regression approach to solve the line spectral estimation problem. In each
iteration process, we first use the ideal of Bayesian lasso to update the sparse vectors; the
derivative of the penalty function forms the regularization parameter. We
choose the anti-trigonometric function as a penalty function to approximate thel0 norm. Then we
use the gradient descent method to update the dictionary parameters. The
theoretical analysis and simulation results demonstrate the effectiveness of
the method and show that the proposed algorithm outperforms other
state-of-the-art methods for many practical cases.