A Super-Resolution Method Based on Iterative Weighted Atomic Norm Minimization for UAV Swarms

Uncrewed aerial vehicle (UAV) swarms have the characteristics of small size, high density, and agile maneuverability. These attributes have given rise to substantial difficulties for radar in achieving precise detection and resolution of UAV swarms. Meanwhile, their potential malicious use poses a significant threat to national security, making accurate identification of UAV swarms of utmost importance. This article presents a super-resolution method for UAV swarms that integrate the coherent long-time integration technique with the gridless sparse recovery method based on iterative weighted atomic norm minimization (IW-ANM). In the method, a framework for UAV swarm detection and super-resolution processing is first established. Then, based on the framework, the IW-ANM algorithm is proposed. This algorithm encodes prior information into the Toeplitz constraint matrix, adopts a well-designed weight function, and finally super-resolves UAV swarms in the spatial dimension through iterative weighting. Numerical simulations demonstrate that, compared with the reweighted atomic norm minimization, ANM, multiple signal classification, and so on, the proposed IW-ANM algorithm is more practical and robust, and has a better super-resolution performance in conditions of low signal-to-noise ratio, high-density swarms, and small angle intervals. Furthermore, a real experiment is conducted to validate the effectiveness of the proposed IW-ANM.