Fast Randomized-MUSIC for mm-Wave Massive MIMO Radars

TL;DR

This paper introduces a fast randomized-MUSIC algorithm that significantly reduces computational complexity in mm-Wave MIMO radars while maintaining high-resolution angle estimation, enabling real-time automotive sensing.

Contribution

The paper presents a novel randomized-MUSIC algorithm using matrix sketching to efficiently estimate the signal subspace with no loss in accuracy.

Findings

Reduces computational time for subspace estimation

Maintains high-resolution AoA estimation accuracy

Validates performance through numerical simulations

Abstract

Subspace methods are essential to high-resolution environment sensing in the emerging unmanned systems, if further combined with the millimeter-wave (mm-Wave) massive multi-input multi-output (MIMO) technique. The estimation of signal/noise subspace, as one critical step, is yet computationally complex and presents a particular challenge when developing high-resolution yet low-complexity automotive radars. In this work, we develop a fast randomized-MUSIC (R-MUSIC) algorithm, which exploits the random matrix sketching to estimate the signal subspace via approximated computation. Our new approach substantially reduces the time complexity in acquiring a high-quality signal subspace. Moreover, the accuracy of R-MUSIC suffers no degradation unlike others low-complexity counterparts, i.e. the high-resolution angle of arrival (AoA) estimation is attained. Numerical simulations are provided to validate the performance of our R-MUSIC method. As shown, it resolves the long-standing contradiction in complexity and accuracy of MIMO radar signal processing, which hence have great potentials in real-time super-resolution automotive sensing.