How to get high resolution results from sparse and coarsely sampled data

TL;DR

This paper introduces a novel exponential analysis method that leverages aliasing from coarse measurements to achieve high-resolution spectral information, improving analysis of clustered frequencies.

Contribution

It presents a new approach that reconditions the spectral recovery problem using aliasing, compatible with various existing multi-exponential analysis techniques.

Findings

Enables high-resolution spectral analysis from coarse, undersampled data.

Effective in distinguishing closely spaced frequency clusters.

Versatile integration with multiple spectral analysis algorithms.

Abstract

Sampling a signal below the Shannon-Nyquist rate causes aliasing, meaning different frequencies to become indistinguishable. It is also well-known that recovering spectral information from a signal using a parametric method can be ill-posed or ill-conditioned and therefore should be done with caution. We present an exponential analysis method to retrieve high-resolution information from coarse-scale measurements, using uniform downsampling. We exploit rather than avoid aliasing. While we loose the unicity of the solution by the downsampling, it allows to recondition the problem statement and increase the resolution. Our technique can be combined with different existing implementations of multi-exponential analysis (matrix pencil, MUSIC, ESPRIT, APM, generalized overdetermined eigenvalue solver, simultaneous QR factorization,...) and so is very versatile. It seems to be especially useful in the presence of clusters of frequencies that are difficult to distinguish from one another.