用深度强化学习优化雷达飞机多目标侦察轨迹，兼顾地形与成像质量。
Multi-stage Planning for Multi-target Surveillance using Aircrafts Equipped with Synthetic Aperture Radars Aware of Target Visibility
- 分三阶段规划：先定访问顺序，再用神经网络预测最佳成像段，最后用3D Dubins曲线连接。
- 在真实地形下生成的轨迹能最大化目标可见性，确保高质量成像。
- 适合实时多目标雷达侦察任务，尤其适用于复杂地形场景。
配备合成孔径雷达（SAR）的飞行器在生成侦察轨迹时面临地形约束及对直线飞行段的需求以保证成像质量。现有方法通常针对预设的直线飞行段进行优化，但未考虑依赖于三维地形和飞行器朝向的目标可见性，且难以扩展至多目标场景。为此，本文提出一种多阶段规划系统：首先估计访问所有目标的航点顺序；其次利用基于深度强化学习训练的新神经网络，预测根据三维地形最大化目标可见性的直线飞行段；最后通过优化生成3D Dubins曲线连接各段，构成完整轨迹。实验表明，该系统在复杂地形下能可靠实现高质量多目标SAR成像，并满足实时性要求。
原文摘要 · Abstract (English)
Generating trajectories for synthetic aperture radar (SAR)-equipped aircraft poses significant challenges due to terrain constraints, and the need for straight-flight segments to ensure high-quality imaging. Related works usually focus on trajectory optimization for predefined straight-flight segments that do not adapt to the target visibility, which depends on the 3D terrain and aircraft orientation. In addition, this assumption does not scale well for the multi-target problem, where multiple straight-flight segments that maximize target visibility must be defined for real-time operations. For this purpose, this paper presents a multi-stage planning system. First, the waypoint sequencing to visit all the targets is estimated. Second, straight-flight segments maximizing target visibility according to the 3D terrain are predicted using a novel neural network trained with deep reinforcement learning. Finally, the segments are connected to create a trajectory via optimization that imposes 3D Dubins curves. Evaluations demonstrate the robustness of the system for SAR missions since it ensures high-quality multi-target SAR image acquisition aware of 3D terrain and target visibility, and real-time performance.
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