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Measuring UAV Propeller Length using Micro-Doppler Signatures

机译:使用微多普勒签名测量无人机螺旋桨长度

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The low radar cross sections of unmanned aerial vehicles (UAVs) pose challenges for their proper classification. Recent works have examined the micro-Doppler (MD) signatures of UAVs for model classification. Here, the physical blade lengths of rotary wings are extracted from the MD blade flash phenomena such that correlated UAV features, such as size and weight, may be deduced to narrow the scope of subsequent classification. The proposed blade length estimation of rotating propellers is applied to simulated and experimentally collected MD measurements to characterize the blade flash phenomena. Assorted UAV blade lengths are experimentally estimated with less than 1% error.
机译:无人机的低雷达横截面对其正确分类提出了挑战。最近的工作已经检查了无人机的微多普勒(MD)签名以进行模型分类。这里,旋转机翼的物理叶片长度是从MD叶片闪光现象中提取的,因此可以推导出相关的UAV特征,例如尺寸和重量,以缩小后续分类的范围。提出的旋转螺旋桨叶片长度估计值应用于模拟和实验收集的MD测量值,以表征叶片飞边现象。实验估计各种无人机叶片的长度误差小于1%。

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