基于因子图的主从式AUV协同定位算法OA
Master-slave AUV cooperative localization algorithm based on factor graph
针对无人自主水下航行器(AUV)集群高精度导航定位需求,提出一种基于因子图(FG)的主从式AUV协同定位算法.针对主从式AUV协同定位系统,构建系统状态方程和量测方程,并在此基础上构建相应因子图模型;根据和积算法(SPA)推导因子图中各节点间消息传递,通过因子图协同定位算法获得从艇位置变量节点概率密度函数(PDF).利用陆上小车、GPS、惯性设备及数据链设备构建一主一从式协同定位试验平台并开展实际试验验证,结果表明:所提因子图协同定位算法相对于常规扩展卡尔曼滤波(EKF)协同定位算法,定位精度提高 18.60%.同时,试验结果也表明测距误差对协同定位精度有较大影响.
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
王苏;黄鸿殿;赵健文;周红进;李倩
海军大连舰艇学院 航海系,大连 116018哈尔滨工程大学 智能科学与工程学院,哈尔滨 150001哈尔滨工程大学 智能科学与工程学院,哈尔滨 150001海军大连舰艇学院 航海系,大连 116018哈尔滨工程大学 智能科学与工程学院,哈尔滨 150001
交通工程
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
autonomous underwater vehiclecooperative localizationfactor graphextended Kalman filterdata link
《北京航空航天大学学报》 2026 (2)
436-444,9
国家自然科学基金(52371368)黑龙江省自然科学基金(YQ2021E011) National Natural Science Foundation of China(52371368)Heilongjiang Provincial Natural Science Foundation of China(YQ2021E011)
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