Superfast Line Spectral Estimation

TL;DR

This paper introduces a Bayesian, low-complexity approach for line spectral estimation that leverages Toeplitz structure to significantly reduce computation time while maintaining high accuracy.

Contribution

It proposes a novel superfast spectral estimation method based on Toeplitz inversion and Bayesian modeling, improving efficiency over existing techniques.

Findings

Achieves estimation accuracy comparable to current methods.

Reduces computation time by orders of magnitude.

Utilizes Toeplitz structure for superfast inversion.

Abstract

A number of recent works have proposed to solve the line spectral estimation problem by applying off-the-grid extensions of sparse estimation techniques. These methods are preferable over 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, thus limiting their practical applicability in cases with large dimensions. 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 Bayesian 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.

Figures