Low Complexity DoA-ToA Signature Estimation for Multi-Antenna Multi-Carrier Systems

TL;DR

This paper introduces low complexity DoA-ToA estimation methods for multi-antenna multi-carrier systems using OFDM signals, combining DFT-based coarse estimation with multistage fine-tuning and a compressed sensing approach for super-resolution, achieving high accuracy with reduced computational load.

Contribution

It proposes novel low complexity DoA-ToA estimation techniques for multi-antenna multi-carrier systems, utilizing DFT and compressed sensing, outperforming traditional methods in computational efficiency.

Findings

Methods achieve similar accuracy to 2D-MUSIC

Significant reduction in computational complexity

Effective super-resolution with 2D-OMP

Abstract

Accurate direction of arrival (DoA) and time of arrival (ToA) estimation is an stringent requirement for several wireless systems like sonar, radar, communications, and dual-function radar communication (DFRC). Due to the use of high carrier frequency and bandwidth, most of these systems are designed with multiple antennae and subcarriers. Although the resolution is high in the large array regime, the DoA-ToA estimation accuracy of the practical on-grid estimation methods still suffers from estimation inaccuracy due to the spectral leakage effect. In this article, we propose DoA-ToA estimation methods for multi-antenna multi-carrier systems with an orthogonal frequency division multiplexing (OFDM) signal. In the first method, we apply discrete Fourier transform (DFT) based coarse signature estimation and propose a low complexity multistage fine-tuning for extreme enhancement in the estimation accuracy. The second method is based on compressed sensing, where we achieve the super-resolution by taking a 2D-overcomplete angle-delay dictionary than the actual number of antenna and subcarrier basis. Unlike the vectorized 1D-OMP method, we apply the low complexity 2D-OMP method on the matrix data model that makes the use of CS methods practical in the context of large array regimes. Through numerical simulations, we show that our proposed methods achieve the similar performance as that of the subspace-based 2D-MUSIC method with a significant reduction in computational complexity.