Multi-kernel unmixing and super-resolution using the Modified Matrix Pencil method

Abstract

Consider $L$ groups of point sources or spike trains, with the $l^{\text{th}}$ group represented by $x_l(t)$. For a function $g:\mathbb{R}\to \mathbb{R}$, let $g_l(t)=g(t/{\mu }_l)$ denote a point spread function with scale ${\mu }_l>0$, and with ${\mu }_1<\cdots <{\mu }_L$. With $y(t)={\sum }_{l=1}^L(g_l\star x_l)(t)$, our goal is to recover the source parameters given samples of $y$, or given the Fourier samples of $y$. This problem is a generalization of the usual super-resolution setup wherein $L=1$; we call this the multi-kernel unmixing super-resolution problem. Assuming access to Fourier samples of $y$, we derive an algorithm for this problem for estimating the source parameters of each group, along with precise non-asymptotic guarantees. Our approach involves estimating the group parameters sequentially in the order of increasing scale parameters, i.e., from group $1$ to $L$. In particular, the estimation process at stage $1\le l\le L$ involves (i) carefully sampling the tail of the Fourier transform of $y$, (ii) a \emph{deflation} step wherein we subtract the contribution of the groups processed thus far from the obtained Fourier samples, and (iii) applying Moitra's modified Matrix Pencil method on a deconvolved version of the samples in (ii).

50 pages, 10 figures, made notational changes and corrected typos after reviewer feedback, to appear in Journal of Fourier Analysis and Applications