Prior-Guided Deep Interference Mitigation for FMCW Radars

Journal Article (2022)
Author(s)

J Wang (TU Delft - Microwave Sensing, Signals & Systems)

Runlong Li (Beijing University of Posts and Telecommunications)

Yuan He (Beijing University of Posts and Telecommunications)

Yang Yang (Tianjin University)

Microwave Sensing, Signals & Systems
Copyright
© 2022 J. Wang, Runlong Li, Yuan He, Yang Yang
DOI related publication
https://doi.org/10.1109/TGRS.2022.3211605
More Info
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Publication Year
2022
Language
English
Copyright
© 2022 J. Wang, Runlong Li, Yuan He, Yang Yang
Microwave Sensing, Signals & Systems
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.@en
Volume number
60
Pages (from-to)
1-16
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Abstract

In this article, the interference mitigation (IM) problem is tackled as a regression problem. A prior-guided deep learning (DL)-based IM approach is proposed for frequency-modulated continuous-wave (FMCW) radars. Considering the complex-valued nature of radar signals, a complex-valued convolutional neural network, which is different from the conventional real-valued counterparts, is utilized as an architecture for implementation. Meanwhile, as the desired beat signals of FMCW radars and interferences exhibit different distributions in the time–frequency domain, this prior feature is exploited as a regularization term to avoid overfitting of the learned representation. The effectiveness and accuracy of our proposed complex-valued fully convolutional network (CV-FCN)-based IM approach are verified and analyzed through both simulated and measured radar signals. Compared with the real-valued counterparts, the CV-FCN shows a better IM performance with a potential of half memory reduction in low signal-to-interference-plus-noise ratio (SINR) scenarios. The average SINR of interfered signals has been improved from −9.13 to 10.46 dB. Moreover, the CV-FCN trained using only simulated data can be directly utilized for IM in various measured radar signals and shows a superior generalization capability. Furthermore, by incorporating the prior feature, the CV-FCN trained on only 1/8 of the full data achieves comparable performance as that on the full dataset in low SINR scenarios, and the training procedure converges faster.

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