Recovery of Sparse 1-D Signals from the Magnitudes of their Fourier Transform

TL;DR

This paper investigates conditions for unique recovery of one-dimensional sparse signals from Fourier magnitude data, proposing two non-iterative algorithms with high-probability recovery guarantees for signals of certain sparsity levels.

Contribution

It introduces two novel non-iterative algorithms for sparse signal recovery from Fourier magnitudes, with theoretical guarantees up to specific sparsity thresholds.

Findings

Combinatorial algorithm recovers signals up to sparsity o(n^{1/3})

Convex optimization algorithm recovers signals up to sparsity o(n^{1/2})

High-probability guarantees for unique recovery under certain conditions

Abstract

The problem of signal recovery from the autocorrelation, or equivalently, the magnitudes of the Fourier transform, is of paramount importance in various fields of engineering. In this work, for one-dimensional signals, we give conditions, which when satisfied, allow unique recovery from the autocorrelation with very high probability. In particular, for sparse signals, we develop two non-iterative recovery algorithms. One of them is based on combinatorial analysis, which we prove can recover signals upto sparsity $o(n^{1/3})$ with very high probability, and the other is developed using a convex optimization based framework, which numerical simulations suggest can recover signals upto sparsity $o(n^{1/2})$ with very high probability.