Abstract
Convolutional neural networks have often been proposed for processing radar Micro-Doppler signatures, most commonly with the goal of classifying the signals. The majority of works tend to disregard phase information from the complex time-frequency representation. Here, the utility of the phase information, as well as the optimal format of the Doppler-time input for a convolutional neural network, is analysed. It is found that the performance achieved by convolutional neural network classifiers is heavily influenced by the type of input representation, even across formats with equivalent information. Furthermore, it is demonstrated that the phase component of the Doppler-time representation contains rich information useful for classification and that unwrapping the phase in the temporal dimension can improve the results compared to a magnitude-only solution, improving accuracy from 0.920 to 0.938 on the tested human activity dataset. Further improvement of 0.947 is achieved by training a linear classifier on embeddings from multiple-formats.
Abstract (translated)
卷积神经网络（CNN）通常被提议用于处理雷达微多普勒特征，最常见的目的是进行信号分类。大多数研究倾向于忽略复杂时间-频率表示中的相位信息。在这里，分析讨论了相位信息在卷积神经网络分类器中的重要性，以及卷积神经网络输入多普勒时间的最优格式。研究发现，卷积神经网络分类器的性能受到输入表示类型的极大影响，即使在具有等效信息的不同格式下也是如此。此外，还证明了多普勒时间表示的相位分量包含对于分类和有用的信息，而在时域中提取相位可以提高结果，与仅依靠幅度的解决方案相比，精度从0.920提高至0.938，在测试的人活动数据集上。通过多格式嵌入的训练，可以实现0.947的进一步改进。
URL
https://arxiv.org/abs/2404.08291
