A number of recent works have proposed to solve the line spectral estimation problem by applying an off-the-grid ex- tension of sparse estimation techniques. These methods are more advantageous than classical line spectral estimation algorithms because they inherently estimate the model order. However, they all have computation times which grow at least cubically in the problem size, which limits their practical applicability for large problem sizes. To alleviate this issue, we propose a low-complexity method for line spectral estimation, which also draws on ideas from sparse estimation. Our method is based on a probabilistic view of the problem. The signal covariance matrix is shown to have Toeplitz structure, allowing superfast Toeplitz inversion to be used. We demonstrate that our method achieves estimation accuracy at least as good as current methods and that it does so while being orders of magnitudes faster. 查看全文>>