一种基于深度学习辅助的SINS/DVL组合导航方法
A Method Based on Deep Learning for Assisting SINS/DVL Integrated Navigation
匡兴红 1黄傲威1
作者信息
- 1. 上海海洋大学工程学院,上海 201306
- 折叠
摘要
水下机器人(autonomous underwater vehicle,AUV)导航定位精度一定上程度影响了 AUV的工作效率,由于GNSS无法在水下使用,以捷联惯导系统/多普勒计程仪(SINS/DVL)的组合导航系统受到众多青睐.DVL在部分情况下会失效,若直接将DVL隔离,系统将变为纯惯性导航系统,严重影响导航定位的精度.为了应对DVL在部分波束缺失的情况,提出了一种DLinear-informer辅助组合导航算法.通过DLinear对原始输入数据特有的分解方式,增强了算法对AUV非线性信息的提取与学习,提高了速度预测精度.实验结果表明:所提算法能够准确预测DVL失效期间丢失波束的速度,降低组合导航的位置误差,提高了系统的鲁棒性和定位精度.
Abstract
The navigation and positioning accuracy of an Autonomous Underwater Vehicle(AUV)affects the efficiency of the AUV to a certain extent,and since GNSS cannot be used underwater,the integrated navigation system of Strapdown Inertial Navigation System/Doppler Velocity Log(SINS/DVL)has been widely favored.However,DVL will fail in some cases,and if DVL is isolated directly,the system will become a pure inertial navigation system,which seriously affects the accuracy of navigation and positioning.In order to cope with the situation that DVL is missing in some beams,a DLinear-Informer assisted integrated navigation algorithm is proposed.Through DLinear's unique decomposition of the original input data,the algorithm enhances the extraction and learning of the nonlinear information of AUV and improves the velocity prediction accuracy.The experimental results show that the proposed algorithm can accurately predict the velocity of the lost beam during the DVL failure,reduce the position error of the integrated navigation,and improve the robustness and positioning accuracy of the system.
关键词
水下机器人/捷联惯导系统/多普勒计程仪/组合导航/波束缺失/DLinear-informerKey words
autonomous underwater vehicle(AUV)/strapdown inertial navigation system/doppler velocity logger/integrated navigation/missing beams/DLinear-informer引用本文复制引用
出版年
2024
NETL
NSTL
万方数据