AI 中文总结
研究针对应急目标定位中现有方法在早期任务阶段表现不佳的问题，提出基于刚性的多无人机轨迹优化方法，通过最大化刚性矩阵最小非零奇异值改善定位，引入剪枝策略实现高效实时，仿真显示该方法有诸多优势。
AI 中文摘要
在车辆和公共安全网络中，减少准确应急呼叫者定位的响应时间至关重要。尽管移动设备常用全球导航卫星系统、Wi-Fi或蜂窝定位，但因信号接收差、基础设施有限和监管限制，其准确性和可用性会降低。基于无人机的定位通过机载传感器协同估计目标位置提供了有前景的替代方案。然而，现有的基于费舍尔信息矩阵（FIM）的轨迹优化方法依赖当前目标估计，在任务早期测量有限且不确定性高时表现不佳。我们提出一种基于刚性的无人机轨迹优化方法，最大化与无人机 - 目标传感图相关的刚性矩阵最小非零奇异值，改善几何条件并减少位置模糊性。我们还引入基于剪枝的矩阵约简策略以实现高效实时实现。仿真表明，与基于FIM的方法相比，该方法搜索时间减少32.9%，更快满足美国联邦通信委员会水平应急定位要求。进一步结果展示了可扩展性、对无人机定位误差和非视距路径损耗的鲁棒性、对航向参数低敏感性、实际计算和通信成本，以及在严重传感和导航扰动下比基于近端策略优化算法的基线更稳定的退化情况。
英文摘要
Reducing the response time for accurate emergency-caller localization is critical in vehicular and public-safety networks. Although mobile devices commonly use GNSS, Wi-Fi, or cellular positioning, their accuracy and availability can degrade because of poor signal reception, limited infrastructure, and regulatory constraints. UAV-based localization offers a promising alternative by using airborne sensors to cooperatively estimate the target position. However, existing Fisher information matrix (FIM)-based trajectory optimization methods depend on the current target estimate and can perform poorly in the early mission stage, when measurements are limited and uncertainty is high. We propose a rigidity-based UAV trajectory optimization method that maximizes the smallest nonzero singular value of the rigidity matrix associated with the UAV-target sensing graph, improving geometric conditioning and reducing position ambiguity. We also introduce a pruning-based matrix reduction strategy for efficient real-time implementation. Simulations show that the proposed method reduces search time by 32.9% compared with FIM-based methods and satisfies the FCC horizontal emergency-localization requirement sooner. Further results demonstrate scalability, robustness to UAV positioning errors and NLOS path loss, low sensitivity to heading parameters, practical computation and communication costs, and more stable degradation than PPO-based baselines under severe sensing and navigation perturbations.
CommentsAccepted for publication in IEEE Transactions on Vehicular Technology
