Attention-GRU神经网络辅助的SINS/DVL组合导航算法
SINS/DVL integrated navigation algorithm assisted by Attention-GRU neural network
王立辉 1刘恩东 1吴璠 1胡桥 2郝程鹏 3吴敏3
作者信息
- 1. 东南大学 仪器科学与工程学院 微惯性仪表与先进导航技术教育部重点实验室,南京 210096
- 2. 西安交通大学 机械工程学院,西安 710049
- 3. 中国科学院 声学研究所,北京 100190
- 折叠
摘要
针对多普勒测速仪(DVL)短时失效导致定位精度下降的问题,提出了一种注意力机制门控循环单元(Attention-GRU)辅助的捷联惯导(SINS)/DVL 组合导航算法.当 DVL 测速有效时,使用SINS/DVL 组合导航信息对 Attention-GRU 神经网络进行训练.当 DVL 故障时,使用训练完毕的Attention-GRU 神经网络预测 DVL 速度辅助校正 SINS.仿真结果表明:当 DVL 故障时,Attention-GRU相对纯惯性导航系统和GRU在匀速运动状态的平均速度误差分别减小了 71.35%和 3.48%,平均位置误差分别减小了34.76%和1.74%;在运动状态变化时平均速度误差分别减小了58.45%和14.67%,平均位置误差分别减小了 9.82%和 2.27%.
Abstract
An algorithm of strapdown inertial navigation system/Doppler velocity log(SINS/DVL)integrated navigation assisted by attention-gated recurrent unit(Attention-GRU)is proposed to address the problem of degraded positioning accuracy caused by temporary failures of DVL in special terrains.During effective DVL measurements,the Attention-GRU neural network is trained by using SINS/DVL integrated navigation information.In the event of DVL failure,the trained Attention-GRU neural network predicts the DVL velocity to assist in correcting the SINS results.Simulation results demonstrate that when DVL is faulty,the Attention-GRU method reduces the average velocity error by 71.35%and 3.48%,and the average position error by 34.76%and 1.74%,respectively,compared with pure inertial navigation and GRU in constant velocity motion.During motion state changes,the Attention-GRU method reduces the average velocity error by 58.45%and 14.67%,and the average position error by 9.82%and 2.27%,respectively,compared with pure inertial navigation and GRU.
关键词
组合导航/自适应卡尔曼滤波/DVL故障/门控循环单元/注意力机制Key words
integrated navigation/adaptive Kalman filtering/DVL failure/gated recurrent unit/attention mechanism引用本文复制引用
基金项目
国家自然科学基金(61773113)
国家自然科学基金(52371337)
国家自然科学基金(62371446)
江苏省重点研发计划(BE2022389)
出版年
2024
NETL
NSTL
万方数据