Cooperative navigation and localization for multiple UUVs

The authors proposed a moving long baseline algorithm based on the extended Kalman filter (EKF) for cooperative navigation and localization of multi-unmanned underwater vehicles (UUVs). Research on cooperative navigation and localization for multi-UUVs is important to solve navigation problems that restrict long and deep excursions. The authors investigated improvements in navigation accuracy. In the moving long base line (MLBL) structure, the master UUV is equipped with a high precision navigation system as a node of the moving long baseline, and the slave UUV is equipped with a low precision navigation system. They are both equipped with acoustic devices to measure relative location. Using traditional triangulation methods to calculate the position of the slave UUV may cause a faulty solution. An EKF was designed to solve this, combining the proprioceptive and exteroceptive sensors. Research results proved that the navigational accuracy is improved significantly with the MLBL method based on EKF.摘 要提出一种基于移动长基线的多无人水下航行器协同导航与定位方法. 多无人水下航行器协同导航与定位方法对于解决大水深和远航程问题至关重要, 在移动长基线算法中, 主 UUV 装备高精度导航设备作为移动长基线节点, 而从 UUV 装备低精度导航设备, 两者之间通过水声装置来相互定位, 利用传统几何关系解算从 UUV 位置的算法将产生很大误差. 基于扩展卡尔曼滤波的移动长基线算法能有效融合内部和外部传感器信息, 提高导航精度. 仿真试验对此进行了验证.

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