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

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 essential components of national security. To address these challenges, we proposes 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 (SNR) and Doppler resolution of UAV signals; then, a low-complexity two-dimensional super-resolution algorithm, called DeRAM, is proposed to super-resolve the UAV swarm targets in range and angle dimensions. The proposed strategy performs three-dimensional integration across space, range, and frequency dimensions, as well as two-dimensional super-resolution in the range and space dimensions, achieving accurate detection and resolution of UAV swarms. The proposed De-RAM algorithm demonstrates superior resolution performance compared to super-resolution techniques such as MFS-RAM, while achieving comparable performance to the RWTM algorithm with significantly reduced computational complexity. Finally, simulation experiments were conducted to verify the authenticity and effectiveness of the proposed strategy.