Title
Interference Mitigation for Automotive FMCW Radar Based on Contrastive Learning With Dilated Convolution
Author
Wang, J. (TU Delft Microwave Sensing, Signals & Systems) 
Li, Runlong (Beijing University of Posts and Telecommunications)
Zhang, Xinqi (Beijing University of Posts and Telecommunications)
He, Yuan (Beijing University of Posts and Telecommunications)
Date
2024
Abstract
As one of the crucial sensors for environment sensing, frequency modulated continuous wave (FMCW) radars are widely used in modern vehicles for driving assistance/autonomous driving. However, the limited frequency bandwidth and the increasing number of equipped radar sensors would inevitably cause mutual interference, degrading target detection and producing safety hazards. In this paper, a deep learning-based interference mitigation (IM) approach is proposed for FMCW radars by using the dilated convolution for network construction and a designated contrast learning strategy for training. The dilated convolution enlarges the receptive field of the neural network, and the designated contrastive learning strategy enforces to distinguish better between interferences and desired signals. The results of numerical simulation and experimental data processing show that the dilated convolution-based IM network, compared to the traditional convolution-based ones, can achieve a higher Signal-to-Interference-plus-Noise-Ratio (SINR) and target detection rate. Moreover, the designated contrastive learning strategy enables a better and more stable IM performance without increasing the complexity of the network, which can facilitate faster signal processing.
Subject
Automotive radar
interference mitigation
deep learning
dilated convolution
contrastive learning
To reference this document use:
http://resolver.tudelft.nl/uuid:e18134a3-5359-4e10-b49b-303085dcbad7
DOI
https://doi.org/10.1109/TITS.2023.3306576
Embargo date
2024-07-31
ISSN
1524-9050
Source
IEEE Transactions on Intelligent Transportation Systems, 25 (1), 545-558
Bibliographical note
Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.
Part of collection
Institutional Repository
Document type
journal article
Rights
© 2024 J. Wang, Runlong Li, Xinqi Zhang, Yuan He