ACTSENA Research Group, Telecommunication Engineering Department, University of Engineering and Technology, Taxila, Punjab 47050, Pakistan.
Department of Electrical Engineering, Polytechnique Montreal, Montreal, QC H3T 1J4, Canada.
Sensors (Basel). 2020 Jul 15;20(14):3923. doi: 10.3390/s20143923.
Unmanned aerial vehicles (UAVs) have become popular in surveillance, security, and remote monitoring. However, they also pose serious security threats to public privacy. The timely detection of a malicious drone is currently an open research issue for security provisioning companies. Recently, the problem has been addressed by a plethora of schemes. However, each plan has a limitation, such as extreme weather conditions and huge dataset requirements. In this paper, we propose a novel framework consisting of the hybrid handcrafted and deep feature to detect and localize malicious drones from their sound and image information. The respective datasets include sounds and occluded images of birds, airplanes, and thunderstorms, with variations in resolution and illumination. Various kernels of the support vector machine (SVM) are applied to classify the features. Experimental results validate the improved performance of the proposed scheme compared to other related methods.
无人飞行器（UAVs）在监控、安全和远程监测方面已经变得非常流行。然而，它们也对公共隐私构成了严重的安全威胁。目前，对于安全保障公司来说，及时检测恶意无人机是一个开放性的研究问题。最近，许多方案已经解决了这个问题。然而，每个方案都有其局限性，例如极端天气条件和巨大的数据集要求。在本文中，我们提出了一个新颖的框架，该框架由混合手工和深度特征组成，用于从声音和图像信息中检测和定位恶意无人机。各自的数据集包括鸟类、飞机和雷暴的声音和遮挡图像，分辨率和光照条件各不相同。支持向量机（SVM）的各种核函数被应用于分类特征。实验结果验证了与其他相关方法相比，所提出的方案具有更好的性能。
