In emergency search and rescue scenarios, the quick location of trapped people is essential. However, disasters can render the Global Positioning System (GPS) unusable. Unmanned aerial vehicles (UAVs) with localization devices can serve as mobile anchors due to their agility and high line-of-sight (LoS) probability. Nonetheless, the number of available UAVs during the initial stages of disaster relief is limited, and innovative methods are needed to quickly plan UAV trajectories to locate non-uniformly distributed dynamic targets while ensuring localization accuracy. To address this challenge, we design a single UAV localization method without hovering, use the maximum likelihood estimation (MLE) method to estimate the location of mobile users and define the upper bound of the localization error by considering users' movement.Combining this localization method and localization error-index, we utilize the enhanced particle swarm optimization (EPSO) algorithm and edge access strategy to develop a low complexity localization-oriented adaptive trajectory planning algorithm. Simulation results demonstrate that our method outperforms other baseline algorithms, enabling faster localization without compromising localization accuracy.

翻译：在紧急搜救场景中，快速定位受困人员至关重要。然而，灾害可能导致全球定位系统（GPS）无法使用。搭载定位设备的无人机凭借其高机动性和高视距通信概率，可作为移动锚点。但灾后初期可用的无人机数量有限，亟需创新方法快速规划无人机航迹，在确保定位精度的同时定位非均匀分布的动态目标。针对这一挑战，我们设计了一种无需悬停的单无人机定位方法，采用最大似然估计法估计移动用户位置，并通过考虑用户运动定义定位误差的上界。结合该定位方法与定位误差指标，我们利用增强型粒子群优化算法和边缘接入策略，开发了低复杂度的定位导向自适应航迹规划算法。仿真结果表明，该方法在不降低定位精度的前提下，定位速度优于其他基线算法。