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.

翻译：装配合成孔径雷达（SAR）飞机生成轨迹面临显著挑战，其原因在于地形约束以及确保高质量成像所需直航段的存在。现有相关研究通常针对预定义的直航段进行轨迹优化，但这些航段无法根据目标可见性（其取决于三维地形与飞机姿态）进行自适应调整。此外，这种假设难以有效扩展至多目标问题——针对该问题，需为实时操作定义能最大化目标可见性的多条直航段。为此，本文提出一种多阶段规划系统：首先，估算访问所有目标的航点序列；其次，采用深度强化学习训练的新型神经网络，根据三维地形预测能最大化目标可见性的直航段；最后，通过施加三维Dubins曲线约束的优化方法连接各航段生成完整轨迹。评估结果表明，该系统能够确保获取感知三维地形与目标可见性的高质量多目标SAR图像，同时满足实时性要求，从而验证了其在SAR任务中的鲁棒性。