多无人机被动探测协同感知可提升复杂环境下多目标隐蔽探测与持续跟踪能力，但被动传感器二维观测信息不完备、目标外观特征弱，导致多目标被动关联与跟踪面临关联特征稀缺、误关联风险高及三维状态估计困难等问题。针对上述瓶颈，本文提出一种混合特征驱动的多无人机多目标被动关联与跟踪方法。首先，针对被动观测下关联特征稀缺、单一关联准则易退化问题，构建融合二维点迹拓扑、航迹运动特性及时空几何约束的混合特征判据，并依据传感器几何配置自适应调整判据权重，实现目标拓扑分布、时序连续性与几何一致性等多源弱关联信息的互补增强，以提升传感器间目标关联准确性与鲁棒性。其次，针对误关联引起的三维多目标状态估计难题，采用高斯混合模型表征关联不确定条件下粗定位融合量测误差的非高斯分布特性，并将其构建为观测因子，引入基于因子图优化的多目标跟踪框架，在滑动窗口内联合观测因子、状态转移因子与目标互斥因子，实现多目标状态鲁棒估计与稳定跟踪。仿真实验结果表明，所提方法在复杂场景下关联精确率达90.7%；在多目标随机运动模式下，定位RMSE较融合跟踪方法和滤波跟踪方法分别降低34.3%和47.7%，多目标跟踪准确率MOTA分别提升44.3%和57.2%。室内缩比试验进一步验证了方法在真实测角噪声和平台运动条件下的工程适用性与跟踪稳定性。研究结果为分布式飞行器多目标被动探测协同感知提供了有效技术路径。

Cooperative perception based on multi-UAV passive detection can enhance covert detection and continuous tracking of multiple targets in complex environments. However, incomplete 2D observations from passive sensors and weak target appearance features lead to key challenges in passive multi-target association and tracking, including association feature scarcity, a high risk of mis-association, and difficulty in 3D state estimation. To address these bottlenecks, this paper proposes a hybrid feature-based passive multi-target data association and tracking method. First, to tackle association feature scarcity under passive observation and the degradation of a single association criterion, a hybrid feature criterion is constructed by integrating 2D point topological distribution, trajectory motion characteristics, and spatiotemporal geometric constraints. The criterion weights are adaptively adjusted according to the sensor geometric configuration, thereby achieving complementary enhancement of multiple weak association cues and improving the accuracy and robustness of inter-sensor target association. Second, to address the difficulty of 3D multi-target state estimation caused by mis-association, the Gaussian Mixture Model is employed to characterize the non-Gaussian distribution of fused measurement errors from coarse localization under association uncertainty. The GMM-based measurement model is formulated as an observation factor and incorporated into a factor graph optimization-based multi-target tracking framework. Within a sliding window, observation factors, state transition factors, and target mutual exclusion factors are jointly optimized to achieve robust estimation and stable tracking of multi-target states. Simulation results show that the proposed method achieves an association precision of 90.7% in complex scenarios. Under multi-target random motion mode, positioning RMSE is reduced by 34.3% and 47.7% compared to fusion-based tracking and filtering-based tracking methods. The multi-target tracking accuracy MOTA is improved by 44.3% and 57.2%. Real-world indoor experiments further verify the engineering applicability and tracking stability of the method. The results provide an effective technical path for multi-target passive detection cooperative perception of distributed aerial vehicles.