Superfast Line Spectral Estimation

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.

16 pages, 7 figures, accepted for IEEE Transactions on Signal Processing

References in corpus (1)

• Variational Bayesian Inference of Line Spectra

Cited by in corpus (9)

• Delay Estimation for Ranging and Localization Using Multiband Channel State Information
• Sequential Detection and Estimation of Multipath Channel Parameters Using Belief Propagation
• Graph-based sequential beamforming
• Fast Variational Block-Sparse Bayesian Learning
• Variational Inference of Structured Line Spectra Exploiting Group-Sparsity
• A Theory of Computational Resolution Limit for Line Spectral Estimation
• Grid-less Variational Bayesian Inference of Line Spectral from Quantized Samples
• Gridless Evolutionary Approach for Line Spectral Estimation with Unknown Model Order
• Cubic NK-SVD: An Algorithm for Designing Parametric Dictionary in Frequency Estimation