Self-Supervised Learning for Enhancing Angular Resolution in Automotive MIMO Radars

A novel framework to enhance the angular resolution of automotive radars is proposed. An approach to enlarge the antenna aperture using artificial neural networks is developed using a self-supervised learning scheme. Data from a high angular resolution radar, i.e., a radar with a large antenna apert...

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Bibliographic Details
Published in:IEEE transactions on vehicular technology 2023-09, Vol.72 (9), p.1-10
Main Authors: Roldan, Ignacio, Fioranelli, Francesco, Yarovoy, Alexander
Format: Article
Language:English
Subjects:
Angular resolution
Antenna arrays
Antennas
Apertures
Artificial neural networks
Automotive engineering
Automotive radar
Machine learning
MIMO
MIMO radar
neural networks
Radar
Radar antennas
Radar cross sections
Radar equipment
radar signal processing
Receiving antennas
Self-supervised learning
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Summary:A novel framework to enhance the angular resolution of automotive radars is proposed. An approach to enlarge the antenna aperture using artificial neural networks is developed using a self-supervised learning scheme. Data from a high angular resolution radar, i.e., a radar with a large antenna aperture, is used to train a deep neural network to extrapolate the antenna element's response. Afterward, the trained network is used to enhance the angular resolution of compact, low-cost radars. One million scenarios are simulated in a Monte-Carlo fashion, varying the number of targets, their Radar Cross Section (RCS), and location to evaluate the method's performance. Finally, the method is tested in real automotive data collected outdoors with a commercial radar system. A significant increase in the ability to resolve targets is demonstrated, which can translate to more accurate and faster responses from the planning and decision-making system of the vehicle.
ISSN:0018-9545
1939-9359
DOI:10.1109/TVT.2023.3269199