Low Complexity Single-Snapshot DoA Estimation via Bayesian Compressive Sensing

Conference paper (2023)

Authors

I. Roldan Montero Microwave Sensing, Signals & Systems - EEMCS
F. Fioranelli Microwave Sensing, Signals & Systems - EEMCS
Alexander Yarovoy Microwave Sensing, Signals & Systems - EEMCS
Research Group
Microwave Sensing, Signals & Systems (EEMCS) (TU Delft)
Copyright: © 2023 I. Roldan Montero, Lucas Lamberti, F. Fioranelli, Alexander Yarovoy
DOI
help_outline
https://doi.org/10.1109/RadarConf2351548.2023.10149589
Antenna arrays Bayesian compressive sensing (BCS) Direction of-arrival estimation (DoA)
More Info
expand_more

Published Date

2023

Language

English

Reuse Rights

Other than for strictly personal use, it is not permitted to download, forward or distribute the text or part of it, without the consent of the author(s) and/or copyright holder(s), unless the work is under an open content license such as Creative Commons.

Faculty

Electrical Engineering, Mathematics and Computer Science

Department

Microelectronics

Research Group

Microwave Sensing, Signals & Systems

Abstract

The problem of single-snapshot direction of arrival (DoA) estimation with antenna arrays has been considered. A sectorized approach based on Bayesian Compressive Sensing (BCS) has been proposed. In this method, the angular space is discretized, defining many non-overlapping small grids which cover the desired large angular space. First, a BCS estimation is run in each of the sectors to estimate the DoA of the signals. Then, a second stage is performed to correct the inconsistencies at the edges due to signal leaking between sectors. The performance of the method has been analyzed via extensive Monte-Carlo simulations in which the number of targets, their Radar Cross Section (RCS), and their location have been varied in a large extent, and the targets were observed by a Frequency Modulated Continuous Wave (FMCW) radar with an 86-element Uniform Linear Array (ULA). The results are compared with state-of-the-art methods in terms of estimation accuracy and resolution. Moreover, an analysis of the computational time, critical for many real-time applications, is presented, which shows a reduction of 20 times in the computational time compared with the standard BCS. Finally, the method has also been validated using experimental data collected with a commercial automotive radar.