Group Iterative Spectrum Thresholding for Super-Resolution Sparse Spectral Selection

TL;DR

This paper introduces a novel regularization method for super-resolution spectral estimation that effectively handles high coherence and noise, utilizing pairing structures and probabilistic screening for efficient, high-resolution frequency selection.

Contribution

It presents a new regularization approach leveraging sine-cosine pairing and probabilistic screening, improving super-resolution spectral estimation in high-coherence, noisy scenarios.

Findings

Effective in high-coherence, noisy environments

Provides high frequency resolution with small samples

Demonstrates computational efficiency in high dimensions

Abstract

Recently, sparsity-based algorithms are proposed for super-resolution spectrum estimation. However, to achieve adequately high resolution in real-world signal analysis, the dictionary atoms have to be close to each other in frequency, thereby resulting in a coherent design. The popular convex compressed sensing methods break down in presence of high coherence and large noise. We propose a new regularization approach to handle model collinearity and obtain parsimonious frequency selection simultaneously. It takes advantage of the pairing structure of sine and cosine atoms in the frequency dictionary. A probabilistic spectrum screening is also developed for fast computation in high dimensions. A data-resampling version of high-dimensional Bayesian Information Criterion is used to determine the regularization parameters. Experiments show the efficacy and efficiency of the proposed algorithms in challenging situations with small sample size, high frequency resolution, and low signal-to-noise ratio.