面向大型无人机与有人机在复杂空域协同运行中面临的高碰撞风险问题，本文提出了一种基于历史轨迹数据与非参数误差建模融合的碰撞概率估算方法，以突破传统模型在误差刻画能力和动态适应性方面的局限性。方法层面，针对轨迹数据非连续性与误差方向难以显性识别的问题，构建基于 B 样条拟合与航向角分解的误差提取机制并通过消融实验确定最优拟合参数。利用核密度估计对导航误差进行建模，从而摆脱固定分布假设对建模精度的限制，并提出了一种可实现任意时刻概率估算的解析模型，并借助蒙特卡洛仿真加以验证。实验结果表明，所提方法在碰撞概率估算中相较传统模型表现出更高的精度与稳定性。进一步结合随机森林方法开展敏感性分析，识别出相对距离为影响风险的主导因子，为面向高动态空域的风险监测与策略优化提供了理论支撑。

Aiming at the high collision risk problem faced by large unmanned aerial vehicles and manned aircraft in the coordinated operation of complex airspace, this paper proposes a collision probability estimation method that integrates historical trajectory data and non-parametric error modeling to break through the limitations of traditional models in terms of error characterization ability and dynamic adaptability. At the method level, in order to solve the problem of discontinuity of trajectory data and difficulty in explicitly identifying the error direction, an error extraction mechanism based on B-spline fitting and heading angle decomposition is constructed, and the optimal fitting parameters are determined through ablation experiments. Kernel density estimation is used to flexibly model the navigation error, thereby getting rid of the limitation of fixed distribution assumption on modeling accuracy, and an analytical model that can realize probability estimation at any time is proposed, which is verified by Monte Carlo simulation. Experimental results show that the proposed method shows higher accuracy and stability in collision probability estimation than traditional models. Further sensitivity analysis is carried out in combination with random forest method, and relative distance is identified as the dominant factor affecting risk, which provides theoretical support for risk monitoring and strategy optimization for highly dynamic airspace.