Detection and Resolution Strategy of UAV Swarms Based on KT-MF and De-RAM

• Published in: IEEE transactions on aerospace and electronic systems Vol. 61; no. 5; pp. 13305 - 13318
• Main Authors: Zhou, Yu, Gou, Liming, Wan, Jun, Guan, Weisen, Xu, Ning, Chen, Zhanye, Huang, Yan
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.10.2025; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Air defense; Algorithms; Autonomous aerial vehicles; Beamforming; Complexity; Computational efficiency; Decoupled reweighted atomic norm minimization (De-RAM); Doppler effect; long-time coherent integration (LTCI) technique; low complexity; Matched filters; Millimeter wave radar; Minimization; National security; Optimization; Radar signal processing; Random access memory; Signal resolution; Signal to noise ratio; super-resolution; Superresolution; unmanned aerial vehicle (UAV) swarms; Unmanned aerial vehicles
• ISSN: 0018-9251; 1557-9603
• DOI: 10.1109/TAES.2025.3578400

Unmanned aerial vehicle (UAV) swarms have gained widespread use in both civil and military sectors due to their small size, high maneuverability, and dense formation. However, these same characteristics can also be exploited maliciously, posing significant risks to national security and presenting a serious challenge to air defense in critical areas. As a result, the detection and resolution of UAV swarms have become the essential components of national security. To address these challenges, we propose a UAV swarm detection and resolution strategy based on the keystone transform matched filter (KT-MF) and decoupled reweighted atomic norm minimization (De-RAM). First, the long-time coherent technology based on digital beamforming technology and KT-MF is introduced to enhance signal-to-noise ratio and Doppler resolution of UAV signals; then, a low-complexity 2-D super-resolution algorithm, called De-RAM, is proposed to super-resolve the UAV swarm targets in range and angle dimensions. The proposed strategy performs 3-D integration across space, range, and frequency dimensions, as well as 2-D super-resolution in the range and space dimensions, achieving the accurate detection and resolution of UAV swarms. The proposed De-RAM algorithm demonstrates superior resolution performance compared with super-resolution techniques, such as modified frequency-selection RAM, while achieving comparable performance to the reweighted trajectory minimization algorithm with significantly reduced computational complexity. Finally, simulation experiments were conducted to verify the authenticity and effectiveness of the proposed strategy.