Spectral Compressed Sensing via Structured Matrix Completion

TL;DR

This paper introduces EMaC, a novel structured matrix completion algorithm for spectral compressed sensing that overcomes basis mismatch issues and achieves near-optimal recovery of spectrally sparse signals from limited samples.

Contribution

The paper develops EMaC, a nonparametric algorithm based on structured low-rank Hankel matrices, providing theoretical guarantees for perfect recovery with fewer samples than traditional methods.

Findings

EMaC achieves perfect recovery with O(r log^2 n) samples.

The method is robust against noise and applicable to super resolution.

Numerical experiments confirm theoretical predictions.

Abstract

The paper studies the problem of recovering a spectrally sparse object from a small number of time domain samples. Specifically, the object of interest with ambient dimension $n$ is assumed to be a mixture of $r$ complex multi-dimensional sinusoids, while the underlying frequencies can assume any value in the unit disk. Conventional compressed sensing paradigms suffer from the {\em basis mismatch} issue when imposing a discrete dictionary on the Fourier representation. To address this problem, we develop a novel nonparametric algorithm, called enhanced matrix completion (EMaC), based on structured matrix completion. The algorithm starts by arranging the data into a low-rank enhanced form with multi-fold Hankel structure, then attempts recovery via nuclear norm minimization. Under mild incoherence conditions, EMaC allows perfect recovery as soon as the number of samples exceeds the order of $\mathcal{O}(r\log^{2} n)$. We also show that, in many instances, accurate completion of a low-rank multi-fold Hankel matrix is possible when the number of observed entries is proportional to the information theoretical limits (except for a logarithmic gap). The robustness of EMaC against bounded noise and its applicability to super resolution are further demonstrated by numerical experiments.