发表机构
SkyGrid（SkyGrid）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对先进空中交通中的动态天气危险，提出一种结合NURBS轨迹拟合与卡尔曼滤波的不确定性预测框架，并采用多面体危险表示和网格相交算法实现飞行前冲突检测。
AI 中文摘要
先进空中交通（AAM）环境中的战略飞行计划验证需要稳健的方法来预测飞机状态不确定性并检测与动态空域危险的潜在冲突。本文提出了一种新颖的框架，将不确定性条件下的轨迹预测与多面体危险表示相结合，用于飞行前冲突检测。我们引入了一种闭式不确定性估计方法，该方法将用于运动学轨迹生成的非均匀有理B样条（NURBS）曲线拟合与用于状态协方差传播的卡尔曼滤波器相结合。借鉴光传播算法（LPA）范式，我们采用了一种S形混合测量噪声模型，该模型捕捉了飞行管理系统在接近航路点所需到达时间（RTA）时的不确定性降低行为。由此产生的时间不确定性边界通过速度到时间方差变换推导得出，从而能够对沿飞行路径的到达时间偏差进行概率评估。对于危险表示，我们开发了一种操作员条件分类方案，该方案将网格化环境数据（特别是天气现象）转换为具有基于强度分层功能的三维多面体体积。这些危险多面体结合了根据飞机性能特征计算出的特定于飞机的安全缓冲区。冲突检测通过网格相交算法执行，该算法作用于平均轨迹周围的空间不确定性管与危险多面体及时间重叠。该框架能够在飞行前规划时间范围内，随着环境条件的演变，对飞行计划进行持续的战略验证。
英文摘要
Strategic flight plan validation in Advanced Air Mobility (AAM) environments requires robust methods for predicting aircraft state uncertainty and detecting potential conflicts with dynamic airspace hazards. This paper presents a novel framework for uncertainty-conditioned trajectory prediction combined with polyhedra hazard representation for pre-flight conflict detection. We introduce a closed-form uncertainty estimation method that couples non-uniform rational B-spline (NURBS) curve fitting for kinematic trajectory generation with a Kalman Filter for state covariance propagation. Drawing from the Light Propagation Algorithm (LPA) paradigm, we employ a sigmoid-blended measurement noise model that captures the uncertainty reduction behavior of flight management systems approaching the required time of arrival (RTA) for waypoints. The resulting temporal uncertainty bounds are derived through a velocity-to-time variance transformation, enabling probabilistic assessment of arrival time deviations along the flight path. For hazard representation, we develop an operator-conditioned classification scheme that transforms gridded environmental data, specifically weather phenomena, into three-dimensional polyhedra volumes with intensity-based stratification. These hazard polyhedra incorporate aircraft-specific safety buffers computed from vehicle performance characteristics. Conflict detection is performed through mesh intersection algorithms operating on the spatial uncertainty tube surrounding the mean trajectory against the hazard polyhedra and temporal overlap. The framework enables the continuous strategic validation of flight plans throughout the pre-flight planning time horizon as environmental conditions evolve.
