北京航空航天大学学报2026，Vol.52Issue(2)：436-444,9.DOI:10.13700/j.bh.1001-5965.2024.0378
基于因子图的主从式AUV协同定位算法
Master-slave AUV cooperative localization algorithm based on factor graph
摘要
Abstract
Using factor graph(FG),a master-slave cooperative localization technique is suggested to meet the high-precision positioning needs of autonomous underwater vehicle(AUV)clusters.First,the state equation and measurement equation for a master-slave AUV cooperative localization system are formulated,and a corresponding FG model is constructed.Second,message passing between nodes within the FG model is derived using the sum-product algorithm(SPA),leading to the acquisition of the probability density function(PDF)for the slave AUV's position.In order to carry out useful experimental verification,a one-master-one-slave cooperative localization test platform is subsequently set up utilizing ground vehicles,GPS,inertial equipment,and data link equipment.The experimental results demonstrate that the proposed cooperative localization algorithm can enhance positioning accuracy by 18.60%compared to the conventional extended Kalman filter(EKF)-based cooperative localization algorithm.Additionally,the results indicate that ranging errors significantly impact the accuracy of cooperative localization关键词
无人自主水下航行器/协同定位/因子图/扩展卡尔曼滤波/数据链Key words
autonomous underwater vehicle/cooperative localization/factor graph/extended Kalman filter/data link分类
交通工程引用本文复制引用
王苏,黄鸿殿,赵健文,周红进,李倩..基于因子图的主从式AUV协同定位算法[J].北京航空航天大学学报,2026,52(2):436-444,9.基金项目
国家自然科学基金(52371368) （52371368）
黑龙江省自然科学基金(YQ2021E011) National Natural Science Foundation of China(52371368) （YQ2021E011）
Heilongjiang Provincial Natural Science Foundation of China(YQ2021E011) （YQ2021E011）
