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Iterative Reweighted l1 Penalty Regression Approach for Line Spectral Estimation

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Document pages: 13 pages

Abstract: In this paper, we proposed an iterative reweighted l1 penaltyregression approach to solve the line spectral estimation problem. In eachiteration process, we first use the ideal of Bayesian lasso to update the sparse vectors; thederivative of the penalty function forms the regularization parameter. Wechoose the anti-trigonometric function as a penalty function to approximate the l0  norm. Then weuse the gradient descent method to update the dictionary parameters. Thetheoretical analysis and simulation results demonstrate the effectiveness ofthe method and show that the proposed algorithm outperforms otherstate-of-the-art methods for many practical cases.

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