Low-Complexity Joint Azimuth-Range-Velocity Estimation for Integrated Sensing and Communication with OFDM Waveform

TL;DR

This paper introduces a low-complexity, high-resolution joint azimuth-range-velocity estimation algorithm for ISAC systems using OFDM waveforms, significantly reducing computational load while enhancing accuracy.

Contribution

It proposes a novel iterative subspace update scheme with Levenberg-Marquardt optimization for super-resolution JARVE, outperforming traditional methods in complexity and accuracy.

Findings

Reduces computational complexity by three orders of magnitude compared to MUSIC.

Decreases complexity by two orders of magnitude compared to ESPRIT.

Improves estimation accuracy in practical 5G NR scenarios.

Abstract

Integrated sensing and communication (ISAC) is a main application scenario of the sixth-generation mobile communication systems. Due to the fast-growing number of antennas and subcarriers in cellular systems, the computational complexity of joint azimuth-range-velocity estimation (JARVE) in ISAC systems is extremely high. This paper studies the JARVE problem for a monostatic ISAC system with orthogonal frequency division multiplexing (OFDM) waveform, in which a base station receives the echos of its transmitted cellular OFDM signals to sense multiple targets. The Cramer-Rao bounds are first derived for JARVE. A low-complexity algorithm is further designed for super-resolution JARVE, which utilizes the proposed iterative subspace update scheme and Levenberg-Marquardt optimization method to replace the exhaustive search of spatial spectrum in multiple-signal-classification (MUSIC) algorithm. Finally, with the practical parameters of 5G New Radio, simulation results verify that the proposed algorithm can reduce the computational complexity by three orders of magnitude and two orders of magnitude compared to the existing three-dimensional MUSIC algorithm and estimation-of-signal-parameters-using-rotational-invariance-techniques (ESPRIT) algorithm, respectively, and also improve the estimation performance.