mmWave Radar Features Extraction of Drones for Machine Learning Classification

Gianluca Ciattaglia, Giulia Temperini, S. Spinsante, E. Gambi
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引用次数: 2

Abstract

With the progressive reduction of cost, in the market it is possible to find a very large assortment of Unmanned Aerial Vehicles (UAV) that are used in general for non-warlike activities. Unfortunately, it may happen that malicious subjects use these objects to cause damage or inconvenience, then the availability of solutions to predict these situations can be crucial for alerting the population and saving lives. In this work, we present a technique to identify drones from their micro-Doppler features, by analyzing their variations during the flight. The characterization of the features and how they evolve in time is useful to predict dangerous situations and classify the drone type, with the help of Machine Learning techniques.
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基于机器学习分类的无人机毫米波雷达特征提取
随着成本的逐步降低,在市场上有可能找到一种非常大的无人驾驶飞行器(UAV),通常用于非战争活动。不幸的是,恶意主体可能会使用这些对象造成损害或不便,那么预测这些情况的解决方案的可用性对于提醒人们和挽救生命至关重要。在这项工作中,我们提出了一种技术,通过分析它们在飞行过程中的变化,从它们的微多普勒特征中识别无人机。在机器学习技术的帮助下,特征的表征及其随时间的演变对于预测危险情况和对无人机类型进行分类是有用的。
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