Interference Mitigation for Automotive FMCW Radar Based on Contrastive Learning With Dilated Convolution

Wang, Jianping; Li, Runlong; Zhang, Xinqi; He, Yuan

doi:10.1109/tits.2023.3306576

Cited by 5 publications

(1 citation statement)

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“…In Ref. [ 39 ], a deep learning method based on contrastive learning was proposed, which enhances the receptive field of the neural network through dilated convolution and trains it with a specific contrastive learning strategy to better distinguish between interference and desired signals. In Ref.…”

Section: Introductionmentioning

confidence: 99%

An Interference Mitigation Method for FMCW Radar Based on Time–Frequency Distribution and Dual-Domain Fusion Filtering

Zhou,

Cao,

Zhang

et al. 2024

Sensors

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Radio frequency interference (RFI) significantly hampers the target detection performance of frequency-modulated continuous-wave radar. To address the problem and maintain the target echo signal, this paper proposes a priori assumption on the interference component nature in the radar received signal, as well as a method for interference estimation and mitigation via time–frequency analysis. The solution employs Fourier synchrosqueezed transform to implement the radar’s beat signal transformation from time domain to time–frequency domain, thus converting the interference mitigation to the task of time–frequency distribution image restoration. The solution proposes the use of image processing based on the dual-tree complex wavelet transform and combines it with the spatial domain-based approach, thereby establishing a dual-domain fusion interference filter for time–frequency distribution images. This paper also presents a convolutional neural network model of structurally improved UNet++, which serves as the interference estimator. The proposed solution demonstrated its capability against various forms of RFI through the simulation experiment and showed a superior interference mitigation performance over other CNN model-based approaches.

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Section: Introductionmentioning

confidence: 99%