Autonomous driving highly depends on capable sensors to perceive the environment and to deliver reliable information to the vehicles' control systems. To increase its robustness, a diversified set of sensors is used, including radar sensors. Radar is a vital contribution of sensory information, providing high resolution range as well as velocity measurements. The increased use of radar sensors in road traffic introduces new challenges. As the so far unregulated frequency band becomes increasingly crowded, radar sensors suffer from mutual interference between multiple radar sensors. This interference must be mitigated in order to ensure a high and consistent detection sensitivity. In this paper, we propose the use of Complex-Valued Convolutional Neural Networks (CVCNNs) to address the issue of mutual interference between radar sensors. We extend previously developed methods to the complex domain in order to process radar data according to its physical characteristics. This not only increases data efficiency, but also improves the conservation of phase information during filtering, which is crucial for further processing, such as angle estimation. Our experiments show, that the use of CVCNNs increases data efficiency, speeds up network training and substantially improves the conservation of phase information during interference removal.

翻译：自主驾驶高度取决于能够感知环境并向车辆控制系统提供可靠信息的感应器。为了提高它的稳健性,使用一套多样化的感应器,包括雷达传感器。雷达是感应信息的一个重要贡献,提供高分辨率和速度测量。在道路交通中更多地使用雷达传感器带来了新的挑战。由于迄今为止不受管制的频率带越来越拥挤,雷达传感器受到多个雷达传感器之间的相互干扰。这种干扰必须减轻,以确保高和一致的探测敏感度。在本文中,我们提议使用复合价值共振神经网络(CVCNNs)来解决雷达传感器之间的相互干扰问题。我们把以前开发的方法推广到复杂的领域,以便根据雷达数据的物理特性处理雷达数据。这不仅提高了数据效率,而且改善了过滤过程中对进一步处理,例如角度估计至关重要的阶段信息的保护。我们的实验表明,CVCNs的使用提高了数据效率,加快了网络培训速度,大大改进了干扰清除过程中阶段信息的保存。