Classification of drones based on micro-Doppler signatures with dual-band radar sensors
Classification of drones based on micro-Doppler signatures with dual-band radar sensors
复制标题
基于双频雷达传感器微多普勒特征的无人机分类
DOI：
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复制
发表时间：
2017
期刊：
影响因子：
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通讯作者：
Gang Li
中科院分区：
文献类型：
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作者：
Pengfei Zhang;Le Yang;Gao Chen;Gang Li
无人机分类因其日益流行和潜在的威胁而变得非常重要。依赖于旋翼叶片旋转的微多普勒特征使我们能够区分不同类型的无人机。为了提高基于微多普勒的无人机分类的稳健性，提出了一种双波段雷达分类方案。首先，分别对K波段和X波段雷达传感器采集的雷达数据进行短时傅立叶变换，得到时频频谱图。然后利用主成分分析(PCA)从时频频谱图中提取特征，并将两个雷达传感器获得的特征融合在一起。最后，利用支持向量机进行分类，得到分类结果。实验结果表明，双波段雷达传感器融合的分类精度高于单一雷达传感器的分类精度。
Drone classification has become of great importance due to its increasing popularity and potential threats. The micro-Doppler signatures depending on the rotation of rotor blades allow us to differentiate various types of drones. To enhance the robustness of micro-Doppler based classification of drones, a dual band radar classification scheme is proposed in this paper. Firstly, the time-frequency spectrograms are obtained by performing the short-time Fourier Transform (STFT) on the radar data collected by K-band and X-band radar sensors respectively. Then the principal components analysis (PCA) is utilized to extract the features from the time-frequency spectrograms, and the features obtained by the two radar sensors are fused together. Finally, the classification results are obtained by using the Support Vector Machine (SVM). The experimental results show that the classification accuracy obtained by the fusion of dual-band radar sensors is higher than that obtained by using only single radar sensor.
影响因子：
4.8
作者：
Fioranelli, Francesco;Ritchie, Matthew;Griffiths, Hugh
通讯作者：
Griffiths, Hugh