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.