Decimated Prony's Method for Stable Super-resolution

TL;DR

This paper introduces a decimated Prony's method that achieves optimal stability and lower computational complexity for super-resolution of signals with closely spaced components, even under noisy conditions.

Contribution

It combines Prony's method with decimation to improve stability and efficiency in super-resolution beyond the Nyquist limit, filling a gap in practical algorithms.

Findings

Achieves asymptotic stability in noisy super-resolution

Reduces computational complexity compared to existing methods

Effective for signals with sub-Nyquist separation

Abstract

We study recovery of amplitudes and nodes of a finite impulse train from noisy frequency samples. This problem is known as super-resolution under sparsity constraints and has numerous applications. An especially challenging scenario occurs when the separation between Dirac pulses is smaller than the Nyquist-Shannon-Rayleigh limit. Despite large volumes of research and well-established worst-case recovery bounds, there is currently no known computationally efficient method which achieves these bounds in practice. In this work we combine the well-known Prony's method for exponential fitting with a recently established decimation technique for analyzing the super-resolution problem in the above mentioned regime. We show that our approach attains optimal asymptotic stability in the presence of noise, and has lower computational complexity than the current state of the art methods.