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摘要:
随着低空经济的蓬勃发展，无人机（UAV）在物流、巡检、监测等领域得到广泛应用，其规模化部署带来了对目标检测与识别能力的更高要求。然而，在实际场景中，密集无人机常导致多目标角度接近、回波能量差异显著，给传统到达角估计方法带来较大挑战。为此，提出一种基于非凸优化的到达角（DOA）估计方法，利用协方差矩阵特征值分析自适应估计近角目标数目，避免对先验信息的依赖；首先构建引入拉普拉斯先验的非凸稀疏约束模型，在保留角度离散化的同时弱化栅格失配影响，并通过迭代优化实现超分辨角度估计；所设计的非凸正则项在强弱目标共存条件下具备抑制主瓣干扰与提升弱目标检测能力的特性，从而增强了对回波能量不一致的鲁棒性。仿真实验表明：所提方法在复杂无人机探测中的有效性与鲁棒性具有更高的估计成功率与估计精度。
Abstract: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.
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图 1 MUSIC、DBF、OMP、ANM和本文方法在三目标近角场景下的DOA谱估计结果
Figure 1. DOA spectrum estimation results of MUSIC, DBF, OMP, ANM and the proposed method in a three closely spaced target scenario
图 2 MUSIC、DBF、OMP、ANM和本文方法在三目标能量不一致场景下的DOA谱估计结果
Figure 2. DOA spectrum estimation results of MUSIC, DBF, OMP, ANM and the proposed method in a three-target scenario with unequal target powers
图 3 MUSIC、DBF、OMP、ANM 及本文方法在不同信噪比下的DOA估计成功率对比结果
Figure 3. Comparison of DOA estimation success rates for MUSIC, DBF, OMP, ANM and the proposed method under different SNRs
图 4 MUSIC、DBF、OMP、ANM 及本文方法在不同信噪比下的DOA估计RMSE对比结果
Figure 4. Comparison of DOA estimation RMSE for MUSIC, DBF, OMP, ANM and the proposed method under different SNRs
表 1 雷达仿真参数
Table 1. Radar simulation parameters
雷达参数 数值 雷达载频$ {f}_{\text{c}} $/ GHz 77 调频斜率$ \mu $/( MHz·μs−1) 58 距离采样数 256 Chirp数 40 脉冲重复周期/μs 100 帧数 300 快拍数 4 带宽$ B $/GHz 1.5 距离分辨率/ m 0.1 速度分辨率/(m·s−1) 0.5 虚拟阵元数 86 角度分辨率/(°) 1.33 
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