针对无人驾驶航空器机载毫米波雷达对“低慢小”目标的高精度角度估计需求，提出一种融合数字波束形成（ DBF）、前后向空间平滑多重信号分类(FBSS-MUSIC)与自适应反馈机制的“粗检-精估-反馈”三阶段分步测角算法。首先，通过DBF在大空域快速粗扫锁定目标方位；其次，在局部小范围内引入FBSS预处理以恢复相干信号下的阵列自由度，并结合赤池信息准则（AIC）/贝叶斯信息准则（ BIC）自适应估计信源数；最后，基于特征值间隙与目标等效信噪比（ SNR）构建置信度评估指标，通过滑动窗口动态调整扫描步长与判决阈值，实现闭环反馈。仿真实验显示，在连续50帧的动态轨迹跟踪中，相较于DBF+MUSIC组合算法，本文算法将平均绝对误差和计算量分别降低38.89%和47.5%，兼顾高精度与实时性。在目标以1、2和4(°)/s速度运动的跟踪实测实验中，本文算法角度估计曲线显著优于DBF、MUSIC及其组合算法，验证了其在实际运行环境下的鲁棒性与工程适用性。

Aiming at the high-precision angle estimation requirement of airborne millimeter-wave radar on unmanned aerial vehicles (UAVs) for "low-slow-small" targets, this paper proposes a three-stage step-by-step angle measurement algorithm of "coarse detection - fine estimation - feedback" integrating digital beamforming (DBF), forward-backward spatial smoothing multiple signal classification (FBSS-MUSIC), and an adaptive feedback mechanism. First, DBF is used to quickly and coarsely scan the large spatial area to lock the target azimuth. Then, FBSS preprocessing is introduced in a local small range to restore the array degrees of freedom under coherent signals, and the Akaike information criterion (AIC)/Bayesian information criterion (BIC) is adaptively applied to estimate the number of signal sources. Finally, based on the eigenvalue gap and the equivalent signal-to-noise ratio (SNR) of the target, a confidence assessment index is constructed. A sliding window is used to dynamically adjust the scanning step size and decision threshold to achieve closed-loop feedback. Simulation experiments show that in the dynamic trajectory tracking of 50 consecutive frames, compared with the DBF + MUSIC combined algorithm, the proposed algorithm reduces the average absolute error and computational load by 38.89% and 47.5%, respectively, achieving a balance between high precision and real-time performance. In the tracking experiments where the target moves at speeds of 1, 2, and 4(°)/s, the angle estimation curve of the proposed algorithm is significantly better than those of DBF, MUSIC, and their combined algorithms, verifying its robustness and engineering applicability in practical operating environments.