Abstract. There is increasing interest in automotive sensor monitoring systems as a means to enhance safety by providing reliable assistance in hazardous situations. These systems are commonly based on video cameras; however, their effectiveness is significantly reduced in adverse weather conditions such as fog, rain, or in the presence of smoke. To address this limitation, radar sensors—particularly imaging radars—are gaining prominence within the context of Driver Assistance Systems. A key challenge in current radar signal processing techniques is their limited ability to distinguish multiple targets along the same line of sight. In this paper, we propose a novel radar signal processing approach based on Deep Learning, capable of detecting and differentiating two or more targets aligned on the same line of sight, while also estimating the position and speed of vehicles ahead. Specifically, we adapt techniques originally developed for civil and military tracking radar applications to the automotive context, taking into account the higher spatial resolution and lower signal-to-noise ratio (SNR) characteristic of automotive radars. The proposed system integrates target detection, tracking, recognition, classification, and analysis, with a particular focus on the accurate identification of close-range targets.
Vitale et al. (2025) studied this question.