为受限转向的追击者设计概率避让区，降低被擒风险。
Probabilistic Weapon Engagement Zones for a Turn Constrained Pursuer
- 基于曲线-直线路径建模追击者运动，推导确定性避让区
- 四种不确定性传播方法评估，神经网络回归精度最优
- 可嵌入轨迹优化，生成考虑追击者不确定性的安全路径
曲线-直线概率交战区（CSPEZ）量化了逃逸者为降低被转向速率受限的追击者捕获风险而应避开的空间区域。该文提出生成最小化捕获风险的逃逸轨迹的方法。首先推导确定性曲线-直线基本交战区（CSBEZ）的解析解，随后采用蒙特卡洛采样、线性化、二次近似和神经网络回归四种不确定性传播方法扩展至概率框架。评估各近似方法的精度与计算成本，并展示如何将CSPEZ约束融入轨迹优化算法，生成显式考虑追击者不确定性的安全路径。
原文摘要 · Abstract (English)
Curve-straight probabilistic engagement zones (CSPEZ) quantify the spatial regions an evader should avoid to reduce capture risk from a turn-rate-limited pursuer following a curve-straight path with uncertain parameters including position, heading, velocity, range, and maximum turn rate. This paper presents methods for generating evader trajectories that minimize capture risk under such uncertainty. We first derive an analytic solution for the deterministic curve-straight basic engagement zone (CSBEZ), then extend this formulation to a probabilistic framework using four uncertainty-propagation approaches: Monte Carlo sampling, linearization, quadratic approximation, and neural-network regression. We evaluate the accuracy and computational cost of each approximation method and demonstrate how CSPEZ constraints can be integrated into a trajectory-optimization algorithm to produce safe paths that explicitly account for pursuer uncertainty.
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