Fourier-sparse interpolation without a frequency gap

TL;DR

This paper introduces a method for estimating Fourier-sparse signals from noisy samples without requiring a minimum frequency gap, achieving efficient interpolation even with off-grid frequencies.

Contribution

It demonstrates that frequency gap assumptions are unnecessary for accurate Fourier-sparse signal estimation, enabling robust interpolation with fewer samples and tolerating noise.

Findings

Estimates Fourier-sparse signals without frequency gap constraints.

Achieves polynomial sample complexity in sparsity and logarithmic in bandwidth.

Provides polynomially efficient interpolation of noisy degree d polynomials.

Abstract

We consider the problem of estimating a Fourier-sparse signal from noisy samples, where the sampling is done over some interval $[0, T]$ and the frequencies can be "off-grid". Previous methods for this problem required the gap between frequencies to be above 1/T, the threshold required to robustly identify individual frequencies. We show the frequency gap is not necessary to estimate the signal as a whole: for arbitrary $k$-Fourier-sparse signals under $\ell_2$ bounded noise, we show how to estimate the signal with a constant factor growth of the noise and sample complexity polynomial in $k$ and logarithmic in the bandwidth and signal-to-noise ratio. As a special case, we get an algorithm to interpolate degree $d$ polynomials from noisy measurements, using $O(d)$ samples and increasing the noise by a constant factor in $\ell_2$.