Accurate localization of maritime targets by unmanned aerial vehicles (UAVs) remains challenging in GPS-denied environments. UAVs equipped with gimballed electro-optical sensors are typically used to localize targets, however, reliance on these sensors increases mechanical complexity, cost, and susceptibility to single-point failures, limiting scalability and robustness in multi-UAV operations. This work presents a new trajectory optimization framework that enables cooperative target localization using UAVs with fixed, non-gimballed cameras operating in coordination with a surface vessel. This estimation-aware optimization generates dynamically feasible trajectories that explicitly account for mission constraints, platform dynamics, and out-of-frame events. Estimation-aware trajectories outperform heuristic paths by reducing localization error by more than a factor of two, motivating their use in cooperative operations. Results further demonstrate that coordinated UAVs with fixed, non-gimballed cameras achieve localization accuracy that meets or exceeds that of single gimballed systems, while substantially lowering system complexity and cost, enabling scalability, and enhancing mission resilience.

翻译：在GPS拒止环境中，利用无人机对海上目标进行精确定位仍然具有挑战性。配备云台光电传感器的无人机通常用于定位目标，然而，对这些传感器的依赖增加了机械复杂性、成本和单点故障的敏感性，限制了多无人机操作的可扩展性和鲁棒性。本研究提出了一种新的轨迹优化框架，该框架能够利用配备固定非云台相机的无人机与水面舰艇协同工作，实现合作目标定位。这种具有估计感知能力的优化方法生成动态可行的轨迹，明确考虑了任务约束、平台动力学和出画事件。与启发式路径相比，估计感知轨迹将定位误差降低了两倍以上，证明了其在协同操作中的优势。结果进一步表明，配备固定非云台相机的协同无人机所达到的定位精度，等于或优于单个云台系统，同时显著降低了系统复杂性和成本，实现了可扩展性，并增强了任务韧性。