Micro-Doppler Signature Analysis for Uav Detection: A Review on Advanced Techniques and Challenges

Abstract

The rapid advancement of drone technology poses a substantial issue in security and airspace management, as standard radar systems struggle to identify and categories unmanned aerial vehicles (UAVs). A key problem is identifying drones from similar sized flying objects, especially in congested situations, indicating a research gap in effective identification systems. This paper looks at sophisticated approaches for drone identification that use micro-doppler signatures (MDS). Iterative adaptive approaches, Short-Timer Fourier Transform (STFT), and machine learning algorithms are all evaluated for their ability to improve classification accuracy. The findings show that combining MDS with multi-static radar systems can result in recognition accuracies greater than 98% for tiny UAVs. These breakthroughs not only advance scientific understanding, but they also have practical applications in law enforcement and public safety. By establishing effective detection systems, we can assure the safe integration of drones into common airspace, fulfilling both security and social requirements.

Keywords: Drone Detection, UAV, Micro doppler, Drone Signature