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

Journal article (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, F. Fioranelli, Alexander Yarovoy
DOI
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https://doi.org/10.1109/TVT.2023.3269199
Machine learning Radar Neural networks MIMO Radar signal processing MIMO radar Automotive radar Antenna arrays Radar antennas Automotive engineering Receiving antennas Radar cross-sections Angular resolution
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Published Date

2023

Language

English

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Faculty

Electrical Engineering, Mathematics and Computer Science

Department

Microelectronics

Research Group

Microwave Sensing, Signals & Systems

Abstract

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.