Spectral Compressive Sensing with Model Selection

TL;DR

This paper introduces a spectral compressive sensing method that uses model selection for joint recovery and estimation, significantly improving accuracy, noise robustness, and computational efficiency over existing techniques.

Contribution

It presents a novel parametric joint recovery-estimation approach based on model selection, addressing limitations of coherence and discretization in spectral CS.

Findings

Outperforms state-of-the-art spectral CS in fidelity

Demonstrates increased noise tolerance

Achieves better computational efficiency

Abstract

The performance of existing approaches to the recovery of frequency-sparse signals from compressed measurements is limited by the coherence of required sparsity dictionaries and the discretization of frequency parameter space. In this paper, we adopt a parametric joint recovery-estimation method based on model selection in spectral compressive sensing. Numerical experiments show that our approach outperforms most state-of-the-art spectral CS recovery approaches in fidelity, tolerance to noise and computation efficiency.