Wideband Interference Suppression for Automotive mmWave CS Radar: From Algorithm-Based to Learning-Based Approaches

In recent times, there has been a significant focus on the development of automotive high-resolution 77 GHz CS (Chirp Sequence) radar, a technology essential for autonomous driving. However, with the increasing popularity of vehicle-mounted CS radars, the issue of intensive inter-radar wideband interference has emerged as a significant concern, leading to undesirable missed targe detection. To solve this problem, various algorithm and learning based approaches have been proposed for wideband interference suppression. In this study, we begin by conducting extensive simulations to assess the SINR (Signal to Interference plus Noise Ratio) and execution time of these approaches in highly demanding scenarios involving up to 7 interfering radars. Subsequently, to validate these approaches could generalize to real data, we perform comprehensive experiments on inter-radar interference using multiple 77 GHz MIMO (Multiple-Input and Multiple-output) CS radars. The collected real-world interference data is then utilized to validate the generalization capacity of these approaches in terms of SINR, missed detection rate, and false detection rate.