Sparse Sampling in Fractional Fourier Domain: Recovery Guarantees and Cram\'er-Rao Bounds

TL;DR

This paper introduces a new time-domain sparse recovery method in the fractional Fourier domain that overcomes spectral leakage issues, supported by a sampling theorem and validated experimentally, along with deriving Cramér-Rao bounds for the problem.

Contribution

It presents a novel time-domain sparse recovery approach in the FrFT domain and establishes Cramér-Rao bounds, advancing theoretical understanding and practical methods.

Findings

New time-domain sparse recovery method avoiding spectral leakage

A sparse sampling theorem for arbitrary FrFT-bandlimited kernels

Validation through hardware experiments and Cramér-Rao bounds derivation

Abstract

Sampling theory in fractional Fourier Transform (FrFT) domain has been studied extensively in the last decades. This interest stems from the ability of the FrFT to generalize the traditional Fourier Transform, broadening the traditional concept of bandwidth and accommodating a wider range of functions that may not be bandlimited in the Fourier sense. Beyond bandlimited functions, sampling and recovery of sparse signals has also been studied in the FrFT domain. Existing methods for sparse recovery typically operate in the transform domain, capitalizing on the spectral features of spikes in the FrFT domain. Our paper contributes two new theoretical advancements in this area. First, we introduce a novel time-domain sparse recovery method that avoids the typical bottlenecks of transform domain methods, such as spectral leakage. This method is backed by a sparse sampling theorem applicable to arbitrary FrFT-bandlimited kernels and is validated through a hardware experiment. Second, we present Cram\'er-Rao Bounds for the sparse sampling problem, addressing a gap in existing literature.