Carrier-free ultra-wideband impulse radars are increasingly used in target detection due to their high resolution and strong interference capabilities. This letter designs a deep residual shrinkage network to achieve the multiangle intelligent vehicle recognition. First, geometric models of the armored car, the truck, and the tank are established. Echo signals under the excitation of the carrier-free ultra-wideband (UWB) signal are obtained. Second, echo signals from different azimuth and pitch angles are arranged and combined into a 2-D matrix so that multiangle information of vehicles is strengthened and embodied. All matrices are converted to grayscale images and adopted as input of subsequent recognition network. Third, a deep residual shrinkage network that utilizes the soft threshold in each channel is designed to recognize vehicles under low signal-to-noise ratios. High precision and outstanding robustness of the designed network is proven by means of comparing with state-of-the-art deep neural networks (DNNs).

中文翻译：

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基于无载波UWB传感器和深度残差收缩学习的车辆多角度识别


无载波超宽带脉冲雷达因其高分辨率和强干扰能力而越来越多地应用于目标检测。本信设计深度残差收缩网络，实现多角度智能车辆识别。首先建立了装甲车、卡车和坦克的几何模型。获得无载波超宽带(UWB)信号激励下的回波信号。其次，将不同方位角和俯仰角的回波信号排列组合成二维矩阵，加强和体现车辆的多角度信息。所有矩阵都转换为灰度图像并作为后续识别网络的输入。第三，设计了利用每个通道中的软阈值的深度残差收缩网络来识别低信噪比下的车辆。通过与最先进的深度神经网络（DNN）的比较，证明了所设计的网络具有高精度和出色的鲁棒性。