Fouriersparse interpolation without a frequency gap
Abstract
We consider the problem of estimating a Fouriersparse signal from noisy samples, where the sampling is done over some interval $[0, T]$ and the frequencies can be "offgrid". 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 $k$Fouriersparse 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 signaltonoise 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 $\ell_2$.
 Publication:

arXiv eprints
 Pub Date:
 September 2016
 arXiv:
 arXiv:1609.01361
 Bibcode:
 2016arXiv160901361C
 Keywords:

 Computer Science  Data Structures and Algorithms
 EPrint:
 FOCS 2016