Cooperative Navigation Method Based on Adaptive CKF for UAVs In GPS Denied Areas

Yingrong Yu, Siting Peng, Qingdong Li, Xiwang Dong, Z. Ren
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引用次数: 1

Abstract

Unmanned Aerial Vehicles (UAVs), when used in a formation setting, can be more advantageous. The concerted operation of UAV formations has many potential applications, such as cooperative reconnaissance, formation combat and search and rescue in mountainous regions. Nevertheless, modern navigation systems for UAVs are not able to guarantee the precision of pose estimation when GPS is unavailable in complex environment. The IMU drifts of navigation systems can cause poor calculation accuracy of position, velocity and attitude of all UAVs in the group within dozens of seconds. To solve this problem, this paper puts forward a new cooperative navigation method based on adaptive Cubature Kalman Filter which shares the relative observations between the UAVs and fuses these data with direct measurements from IMU to obtain better navigation performance. The simulation results demonstrate the validity of the proposed method.
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GPS拒止区无人机自适应CKF协同导航方法
无人驾驶飞行器(uav),当用于编队设置时,可以更有利。无人机编队协同作战具有协同侦察、编队作战、山区搜救等多种潜在应用。然而,现代无人机导航系统在复杂环境下没有GPS时,无法保证姿态估计的精度。导航系统的IMU漂移会导致群内所有无人机在几十秒内的位置、速度和姿态计算精度较差。针对这一问题,本文提出了一种基于自适应Cubature Kalman滤波的协同导航方法,该方法通过共享无人机之间的相关观测数据,并将这些数据与IMU的直接测量数据融合,以获得更好的导航性能。仿真结果验证了该方法的有效性。
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