Low-Complexity Algorithms for Multichannel Spectral Super-Resolution

TL;DR

This paper introduces low-complexity algorithms for multichannel spectral super-resolution that leverage low-rank matrix structures and multichannel information, demonstrating superior performance through extensive simulations.

Contribution

It proposes two novel optimization algorithms based on low-rank Hankel-Toeplitz matrix factorization tailored for multichannel spectral super-resolution.

Findings

Algorithms outperform existing methods in simulations

Effective exploitation of multichannel and CA structures

Low computational complexity achieved

Abstract

This paper studies the problem of multichannel spectral super-resolution with either constant amplitude (CA) or not. We propose two optimization problems based on low-rank Hankel-Toeplitz matrix factorization. The two problems effectively leverage the multichannel and CA structures, while also enabling the design of low-complexity gradient descent algorithms for their solutions. Extensive simulations show the superior performance of the proposed algorithms.