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.
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