Identification of human individuals within a group of 39 persons using micro-Doppler (μ-D) features has been investigated. Deep convolutional neural networks with two different training procedures have been used to perform classification. Visualization of the inner network layers revealed the sections of the input image most relevant when determining the class label of the target. A convolutional block attention module is added to provide a weighted feature vector in the channel and feature dimension, highlighting the relevant μ-D feature-filled areas in the image and improving classification performance.

中文翻译：

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使用雷达微多普勒频谱图识别人类步态的基于注意力的深度学习网络

使用微多普勒识别一组 39 人中的个人（μ-D) 特性已被研究。具有两种不同训练程序的深度卷积神经网络已被用于执行分类。在确定目标的类标签时，内部网络层的可视化揭示了输入图像中最相关的部分。增加了卷积块注意力模块，在通道和特征维度上提供加权特征向量，突出相关μ-D 图像中的特征填充区域并提高分类性能。