基于移动长基线的多AUV协同导航

Cooperative Navigation of Multiple AUVs using Moving Long Baseline

基于扩展卡尔曼滤波(EKF)理论研究了多AUV协同导航定位的移动长基线算法.移动长基线多AUV协同导航结构中,主AUV内部装备高精度导航设备,从AUV内部装备低精度导航设备,外部均装备水声装置测量相对位置关系,利用移动长基线算法融合内部和外部传感器信息,实时获取从AUV的位置信息.建立了协同导航系统数学模型,设计了EKF协同导航算法,在各种测试情况下通过仿真验证了所推导的分析结果,对EKF和几何解方程算法的导航效果进行了比较.研究结果表明,以主AUV作为移动的长基线节点时,通过EKF算法可以显著提高群体的导航定位精度.

Moving long baseline （MLBL） algorithm based on extended Kalman filter （EKF） for cooperative navigation and localization of multiple AUVs （autonomous underwater vehicles） is proposed. In MLBL-based multi-AUV cooperative navigation structure, the master AUV is equipped with high precision navigation system, and the slave AUV is equipped with low precision navigation system. They both are equipped with acoustic devices to measure relative location. The moving long baseline algorithm combines proprioceptive and exteroceptive information, and gets the real time position of slave AUV. The math model of cooperative navigation system is developed, and a cooperative navigation algorithm based on extended Kalman filter is designed. The analytical results derived in this paper are validated with simulation in different test cases, and navigation performances of EKF algorithm and geometric equation algorithm are compared. The research results prove that the navigation accuracy of AUV group is improved effectively by using EKF algorithm in the condition that master AUV is used as moving long baseline node.