The problem of cooperative localization for a small group of unmanned aerial vehicles (UAVs) in a Global Navigation Satellite System-denied environment is addressed in this paper. The presented approach contains two sequential steps: first, an algorithm called cooperative ranging localization, formulated as an extended Kalman filter, estimates each UAV’s relative pose inside the group using intervehicle ranging measurements; second, an algorithm named cooperative magnetic localization, formulated as a particle filter, estimates each UAV’s global pose through matching the group’s magnetic anomaly measurements to a given magnetic anomaly map. In this study, each UAV is assumed to only perform a ranging measurement and data exchange with one other UAV at any point in time. A simulator is developed to evaluate the algorithms with magnetic anomaly maps acquired from airborne geophysical survey. The simulation results show that the average estimated position error of a group of 16 UAVs is approximately 20 m after flying about 180 km in 1 h. Sensitivity analysis shows that the algorithms can tolerate large variations of velocity, yaw rate, and magnetic anomaly measurement noises. Additionally, the UAV group shows improved position estimation robustness with both high- and low-resolution maps as more UAVs are added into the group.

本文解决了在全球导航卫星系统被拒绝的环境中，一小群无人机的协同定位问题。所提出的方法包括两个连续步骤：首先，一种称为协作测距定位的算法，公式化为扩展的卡尔曼滤波器，它使用车辆测距测量来估计组内每个无人机的相对姿态。其次，称为协作磁定位的算法（被构造为粒子滤波器）通过将小组的磁异常测量值与给定的磁异常图进行匹配来估计每个无人机的全局姿态。在这项研究中，假设每架无人机在任何时间点都只能与另一架无人机进行测距测量和数据交换。开发了一个仿真器，以通过从机载地球物理勘测中获取的磁异常图来评估算法。仿真结果表明，一组16架无人机在1小时内飞行约180 km后的平均估计位置误差约为20 m。敏感性分析表明，该算法可以容忍速度，偏航率和磁异常测量噪声的较大变化。此外，随着更多的无人机被添加到该组中，UAV组在高分辨率和低分辨率地图上均显示出改进的位置估计鲁棒性。和磁异常测量噪声。此外，随着更多的无人机被添加到该组中，UAV组在高分辨率和低分辨率地图上均显示出改进的位置估计鲁棒性。和磁异常测量噪声。此外，随着更多的无人机被添加到该组中，UAV组在高分辨率和低分辨率地图上均显示出改进的位置估计鲁棒性。