用固定相机无人机协同定位海上目标，降低系统复杂度
Multi-UAV Trajectory Optimization for Bearing-Only Localization in GPS Denied Environments
- 设计动态可行轨迹优化框架，考虑任务约束与平台动力学
- 定位误差比启发式路径降低两倍以上，精度媲美单个云台相机
- 适合需要低成本、高可靠性的多无人机海上监视场景
在无GPS环境下，无人飞行器（UAV）对海上目标的精确定位仍具挑战性。传统方法依赖云台式光电传感器，但会增加机械复杂度、成本及单点故障风险，限制多无人机系统的可扩展性与鲁棒性。本文提出一种新的轨迹优化框架，使配备固定非云台相机的无人机与水面舰船协同工作，实现合作目标定位。该估计感知优化生成动态可行轨迹，显式考虑任务约束、平台动力学和目标出框事件。结果表明，估计感知轨迹相比启发式路径将定位误差降低超过两倍，且协调的固定相机无人机系统在定位精度上达到或超过单个云台系统水平，同时显著降低系统复杂度与成本，提升可扩展性与任务韧性。
原文摘要 · Abstract (English)
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.
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