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Fang, J., Li, J., Shen, Y., Li, H. and Li, S. (2014) Super-Resolution Compressed Sensing: An Iterative Reweighted Algorithm for Joint Parameter Learning and Sparse Signal Recovery. IEEE Transactions on Signal Process, 21, 761-765.
https://doi.org/10.1109/LSP.2014.2316004

has been cited by the following article:

• 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.