Stable super-resolution limit and smallest singular value of restricted Fourier matrices

TL;DR

This paper analyzes the super-resolution limit for recovering closely spaced point sources from noisy Fourier data, deriving bounds on the singular values of Vandermonde matrices that depend explicitly on the super-resolution factor and source configuration.

Contribution

It introduces a novel clumps model for closely spaced sources and provides exact super-resolution factor-dependent bounds on the minimum singular value of Vandermonde matrices.

Findings

Derived non-asymptotic lower bounds for singular values based on source configuration.

Established the dependence of MUSIC algorithm sensitivity on super-resolution factor.

Validated theoretical bounds through numerical experiments.

Abstract

We consider the inverse problem of recovering the locations and amplitudes of a collection of point sources represented as a discrete measure, given $M+1$ of its noisy low-frequency Fourier coefficients. Super-resolution refers to a stable recovery when the distance $\Delta$ between the two closest point sources is less than $1/M$. We introduce a clumps model where the point sources are closely spaced within several clumps. Under this assumption, we derive a non-asymptotic lower bound for the minimum singular value of a Vandermonde matrix whose nodes are determined by the point sources. Our estimate is given as a weighted $\ell^2$ sum, where each term only depends on the configuration of each individual clump. The main novelty is that our lower bound obtains an exact dependence on the {\it Super-Resolution Factor} $SRF=(M\Delta)^{-1}$. As noise level increases, the {\it sensitivity of the noise-space correlation function in the MUSIC algorithm} degrades according to a power law in $SRF$ where the exponent depends on the cardinality of the largest clump. Numerical experiments validate our theoretical bounds for the minimum singular value and the sensitivity of MUSIC. We also provide lower and upper bounds for a min-max error of super-resolution for the grid model, which in turn is closely related to the minimum singular value of Vandermonde matrices.