This paper presents a learning-based framework for estimating pursuer parameters in turn-rate-limited pursuit-evasion scenarios using sacrificial agents. Each sacrificial agent follows a straight-line trajectory toward an adversary and reports whether it was intercepted or survived. These binary outcomes are related to the pursuer's parameters through a geometric reachable-region (RR) model. Two formulations are introduced: a boundary-interception case, where capture occurs at the RR boundary, and an interior-interception case, which allows capture anywhere within it. The pursuer's parameters are inferred using a gradient-based multi-start optimization with custom loss functions tailored to each case. Two trajectory-selection strategies are proposed for the sacrificial agents: a geometric heuristic that maximizes the spread of expected interception points, and a Bayesian experimental-design method that maximizes the D-score of the expected Gauss-Newton information matrix, thereby selecting trajectories that yield maximal information gain. Monte Carlo experiments demonstrate accurate parameter recovery with five to twelve sacrificial agents. The learned engagement models are then used to generate safe, time-optimal paths for high-value agents that avoid all feasible pursuer engagement regions.

翻译：本文提出一种基于学习的框架，用于在转向速率受限的追逃场景中利用牺牲智能体估计追击者参数。每个牺牲智能体沿直线轨迹向对手运动，并报告其是否被拦截或幸存。这些二元结果通过几何可达区域模型与追击者参数相关联。本文引入两种建模方式：边界拦截情形（捕获发生在可达区域边界）和内部拦截情形（允许在可达区域内任意位置捕获）。追击者参数通过基于梯度的多起点优化方法进行推断，并针对每种情形设计了定制化的损失函数。为牺牲智能体提出了两种轨迹选择策略：一种是最大化预期拦截点散布范围的几何启发式方法，另一种是贝叶斯实验设计方法，该方法通过最大化预期高斯-牛顿信息矩阵的D分数来选择能产生最大信息增益的轨迹。蒙特卡洛实验表明，使用五至十二个牺牲智能体即可实现精确的参数恢复。学习得到的交战模型随后被用于为高价值智能体生成避开所有可行追击者交战区域的安全时间最优路径。