用微多普勒谱图识别金属材质，准确率超95%。
SDR-Based Metal Classification using Spectrogram Images from Micro-Doppler Signatures
- 通过微多普勒信号生成谱图，提取振动特征进行分类。
- 基于CNN模型在实验室数据上实现>95%分类准确率。
- 适合工业与国防领域的非接触式材料识别应用。
铜、黄铜和铝等金属广泛应用于工业制造中，其振动特性独特，可用于远距离识别。本研究提出一种基于微多普勒签名的金属分类方法，利用软件定义无线电（SDR）在受控实验中采集振动金属板的回波信号，生成谱图图像。通过几何变换对谱图数据进行增强，并训练卷积神经网络（CNN）模型进行分类。结果表明，该模型在识别黄铜、铜和铝三种金属时，分类准确率超过95%。该研究为利用微多普勒谱图图像进行材料识别提供了基础，可拓展至工业与国防领域的传感能力提升。
原文摘要 · Abstract (English)
Metallic materials such as brass, copper, and aluminum are used in numerous applications, including industrial manufacturing. The vibration characteristics of these objects are unique and can be used to identify these objects from a distance. This research presents a methodology for detecting and classifying these metallic objects using the vibration dynamics induced by their micro-Doppler signatures. The proposed approach utilizes image processing techniques to extract pivotal features from spectrograms. These spectrograms originate from micro-Doppler signatures of data collected during controlled laboratory experiments where signals were transmitted towards vibrating metal sheets, and the ensuing reflections were recorded using a software-defined radio (SDR). The spectrogram data was augmented using geometric transformation to train a convolutional neural network (CNN) based machine learning model for object classification. The results indicate that the proposed CNN model achieved an accuracy of more than 95% in classifying metals into brass, copper, and aluminum. This research could be used to understand the foundations of classifying spectrogram images using micro-Doppler signatures for its applications towards enhancing the sensing capabilities in industrial and defense applications.
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