密集无人机目标雷达非凸DOA估计方法OA

With the rapid development of the low-altitude economy,unmanned aerial vehicles(UAV)have been widely deployed in fields such as logistics,inspection,and surveillance.Their large-scale deployment imposes increasing demands on target detection and identification capabilities.However,in real-world situations,UAV swarms frequently result in widely separated arrival angles and notable variations in echo energy,which provide major difficulties for conventional direction of arrival(DOA)estimate techniques.To address this,this paper proposes a DOA estimation algorithm based on non-convex optimization.First,the number of closely spaced targets is adaptively estimated via eigenvalue analysis of the covariance matrix,thereby avoiding dependence on prior information.Then,a non-convex sparse constraint model incorporating a Laplacian prior is constructed,which preserves angle discretization while mitigating the off-grid effect,and achieves super-resolution angle estimation through iterative optimization.In the presence of coexisting strong and weak targets,the suggested non-convex regularization term can improve robustness to echo power imbalance by suppressing main-lobe interference and increasing weak target detection.Simulation results validate the effectiveness and robustness of the proposed method in complex UAV detection scenarios,demonstrating higher estimation accuracy and success rate.