Unmanned Aerial Vehicles, or drones, pose a significant threat to privacy and security. To understand and assess this threat, classification between different drone models and types is required. One way in which this has been demonstrated experimentally is through this use of micro-Doppler information from radars. Classifiers capable of exploiting differences in micro-Doppler spectra will require large amounts of data but obtaining such data experimentally is expensive and time consuming. The authors present the methodology and results of a drone micro-Doppler simulation framework which uses accurate 3D models of drone components to yield detailed and realistic synthetic micro-Doppler signatures. This is followed by the description of a purpose-built validation radar that has been developed specifically to gather high-fidelity experimental drone micro-Doppler data with which is used to validate the simulation. Detailed comparisons between the experimental and simulated micro-Doppler spectra from three models of drones with differently shaped propellers are given, showing very good agreement. The aim is to introduce the simulation methodology. Validation using single propeller micro-Doppler is provided, although the simulation can be extended to multiple propellers. The simulation framework offers the potential to generate large quantities of realistic drone micro-Doppler signatures for training classification algorithms.

中文翻译：

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一种通过实验验证生成精确无人机微多普勒的新模拟方法

无人驾驶飞行器或无人机对隐私和安全构成重大威胁。为了了解和评估这种威胁，需要对不同无人机型号和类型进行分类。实验证明这一点的一种方法是使用来自雷达的微多普勒信息。能够利用微多普勒频谱差异的分类器将需要大量数据，但通过实验获得此类数据既昂贵又耗时。作者介绍了无人机微多普勒模拟框架的方法和结果，该框架使用无人机组件的精确 3D 模型来生成详细且真实的合成微多普勒特征。接下来是对专用验证雷达的描述，该雷达是专门为收集高保真实验无人机微多普勒数据而开发的，用于验证模拟。对具有不同形状螺旋桨的三种无人机模型的实验和模拟微多普勒频谱进行了详细比较，显示出非常好的一致性。目的是介绍模拟方法。尽管模拟可以扩展到多个螺旋桨，但提供了使用单螺旋桨微多普勒的验证。该模拟框架提供了生成大量真实无人机微多普勒特征以用于训练分类算法的潜力。