Multiantenna Radar Signal Interference Mitigation Using Complex-Valued Convolutional Neural Networks
比较了不同卷积神经网络变体在多天线雷达数据中抑制相互干扰的能力，提出一种改进的复值CNN架构，能显著提升目标检测、相位重建和角度估计性能。
Modern vehicles increasingly rely on sensors to monitor their environment and to support driver assistance and safety systems. Most vehicles use a variety of different sensors to improve robustness. A vital part of these is the radar sensor. It provides the vehicle not only with location but also with valuable velocity information from surrounding objects. The increasing usage of radar systems in road traffic also causes problems in terms of mutual interference between different radar sensors. This interference leads to broadband disturbances in the signal which must be mitigated to ensure reliable object detection and object angle estimation. In this article, we compare different variants of convolutional neural networks (CNNs) in their ability to mitigate mutual interference for multiantenna radar data. We analyze the potential of using multiantenna data for real-valued CNN (RVCNN) and complex-valued (CVCNN) models, comparing detection, phase reconstruction, and angle estimation performances. Furthermore, we propose a complex-valued CVCNN (CVCNN) architecture using a modified batch normalization method that omits activation scaling. Our experiments show, that using multiantenna data in combination with CVCNNs can greatly improve detection, phase, as well as angle estimation performance and that activation scaling is detrimental to our CVCNN architecture.