Adversarial interference mitigation for automotive radar

Jiang, Chenming; Chen, Tianyi; Yang, Bin

doi:10.1109/radarconf2147009.2021.9455216

Cited by 10 publications

(1 citation statement)

References 18 publications

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“…[22] processed the range-Doppler spectrogram of FMCW radar using a simple Convolutional Neural Network (CNN) to suppress interference and noise. Meanwhile, some researchers also adopted more complex convolutional neural networks such as U-Net [23] and Generative Adversarial Networks (GAN) [24] to learn interference features from radar signal spectrograms and eliminate these interference features from received radar signals, thereby achieving filtering. Reference [25] proposed a method using complex convolutional neural networks to learn the mapping relationship between the original complex signal and the denoised complex signal, and obtained the filtered signal using short-time Fourier inverse transform.…”

Section: Introductionmentioning

confidence: 99%

An Adaptive Filtering Algorithm Based on Range-Doppler Information Guidance

Li,

Zhang,

et al. 2023

IEEE Access

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Millimetre-wave frequency-modulated continuous-wave (FMCW) radar is widely used in various scenarios. However, it is often affected by static and dynamic clutter interference, which has a negative impact on its performance. Specifically, these clutter signals are often mistaken for target signals, leading to false detection and affecting the accuracy of target tracking and localization. In addition, dynamic clutter sources, such as other moving objects, also bring about Doppler frequency shift interference, further affecting the measurement of target velocity. In this paper, addressing the issue of static clutter, we propose a frame mean subtraction method. Additionally, for the more complex problem of dynamic clutter, we introduce a filtering approach guided by distance-Doppler information. This method utilizes a mask generated in real-time by tracking the temporal distance information of the target as prior information for filtering radar signals. Subsequently, we employ a novel fractional short-time Fourier transform to extract the Doppler feature spectrogram of the radar signal. Finally, a ResNet-50 model trained on the Doppler spectrograms of interference-free radar signals is used to test the Doppler maps generated from the filtered radar signals. After testing, the classification accuracy reaches 97.5%. This result shows that the micro-Doppler spectrum obtained by filtering the radar signal collected in complex scenes using the proposed method is highly similar to the micro-Doppler spectrum of the target to be measured. In addition, the proposed filtering method not only plays the role of signal filtering, but also enhances the strength of the target signal and provides more detailed information for the subsequent recognition task.INDEX TERMS LFMCW, micro-doppler, radar signal filtering, convolutional neural network.

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