%0 Journal Article %T Iterative Reweighted l1 Penalty Regression Approach for Line Spectral Estimation %A Fei Ye %A Xian Luo %A Wanzhou Ye %J Advances in Pure Mathematics %P 155-167 %@ 2160-0384 %D 2018 %I Scientific Research Publishing %R 10.4236/apm.2018.82008 %X In this paper, we proposed an iterative reweighted l1 penalty 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 the l0 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. %K Line Spectral Estimation %K Penalty Regression %K Bayesian Lasso %K Iterative Reweighted Approach %U http://www.scirp.org/journal/PaperInformation.aspx?PaperID=82602