Detection and Tracking of UAV Targets Using Deep Learning


  • Mohamed Khedir Noraldain Alamin Department of computer, Faculty of engineering, AL-Neelain University



UAV, DeepLearning, RCS, Micro-Doppler Signature.


In recent years, the use of Flying drones and modern Unmanned aerial vehicles (UAVs) with the latest techniques and capabilities for both civilian and military applications growing sustainably on a large scope, Drones could autonomously fly in several environments and locations and could perform various missions, providing a system for UAV detection and tracking represent crucial importance. This paper discusses Designing Detection and Tracking method as a part of Aero-vehicle Defense System (ADS) for UAVs using Deep learning algorithms. The small Radar cross-section (RCS) foot-print makes a problem for Traditional methods and Aero-vehicle Defense systems to distinguish between birds, stealth fighters, and UAVs incomparable of size and RCS characteristics, the detection is a challenge in low RCS targets because the chance of detection is incredibly less moreover, in the existence of interference and clutter which reduce the performance of detection process rapidly. 


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How to Cite

Noraldain Alamin, M. K. (2021). Detection and Tracking of UAV Targets Using Deep Learning. Journal of Karary University for Engineering and Science, 1(2).