Automotive Radar Interference Mitigation Based on a Generative Adversarial Network

This paper introduces a novel automotive radar interference mitigation approach using a generative adversarial network (GAN). Instead of tackling the mutual interference in the time domain, a generative adversarial network is trained and used to recover the complex signal in the frequency domain, namely on the complex range profile obtained after the fast Fourier transform of fast-time samples (RFFT spectrum). It is shown that by employing the gated convolution and an attention mechanism, the generator network has the ability to learn the amplitude and phase information for missing data from the remaining signal. Experimental results show that the proposed method can provide a remarkable improvement in signal-to-interference-plus-noise ratio (SINR) and preserves its robustness in severely disturbed scenarios that are much more complex than the training dataset.