Interference Suppression for Automotive Millimeter-Wave Radars With Fused Lasso

Millimeter-wave radars are widely used in advanced driver assistance systems (ADAS) and autonomous driving. The suppression of mutual interference is challenging for automotive radars to address. The traditional sparse-based methods are effective in interference suppression but have a high computational overhead. However, automotive radars are sensitive to computational overhead. To tackle the problem, this paper proposes a fast convergence method based on the fused Lasso. Taking advantage of the pulse-like shape of the mutual interference in the time domain, a fused Lasso based sparse model is proposed to extract and eliminate the mutual interference. Experiments on simulated and real radar data are conducted to verify the effectiveness of the proposed method, and experiments in dynamic scenarios are conducted. In the two-dimensional range-Doppler (RD) map, the increased signal to interference plus noise ratio (SINR) of the proposed method is about 11.95 dB. In addition, the time cost of the proposed method is much lower than the traditional methods. The robustness of the proposed method is also verified via experiments with different chirp rates and pulse durations.