摘要
[目的]针对船舶运动状态变化复杂场景下扩展卡尔曼滤波(EKF)的误差不稳定,以及单一运动模型的表征能力受限等问题,提出一种基于交互式多模型(IMM)平方根容积卡尔曼滤波(SCKF)的船舶轨迹跟踪算法。[方法]引入SCKF,并代替EKF来执行自动识别系统(AIS)数据的轨迹跟踪;采用交互式多模型框架将恒速直线模型(CVM)、当前统计模型(CSM)和恒定转向率模型(CTM)及改进的CTM模型进行交互融合,形成3种组合模型来表征AIS轨迹的运动状态,并进行船舶轨迹跟踪实验。[结果]结果显示,对于航向、航向率和航速均发生变化的轨迹,采用组合模型1跟踪时,在轨迹6中SCKF相比EKF的位置信息的均方根误差变化幅度小,精度提高了30.06%;采用组合模型3跟踪时,相比EKF,SCKF在轨迹6中位置信息的均方根误差波动的范围最小,误差减小了60.80%,组合模型3的性能最好,但计算量也最大;对于航速不发生变化的复杂轨迹,采用组合模型2跟踪的性能接近组合模型3。[结论]所提方法能够提高AIS数据的精度并保证AIS数据误差波动的稳定性,为提高船舶运动跟踪和监测提供了可能性。
[Objectives]Aiming at the unstable error of the extended Kalman filter(EKF)and the limited representation ability of a single motion model in a scenario involving complex changes of a ship’s motion state,a ship trajectory tracking algorithm based on an interactive multi-model(IMM)square root cubature Kalman filter(SCKF)is proposed.[Methods]The SCKF is introduced to replace the EKF in performing the trajectory tracking of automatic identification system(AIS)data;the constant velocity model(CVM),current statistical model(CSM),constant turn rate model(CTM)and improved CTM are combined using an interactive multi-model framework,and three combined models are constructed to characterize the motion state of the AIS trajectory.Trajectory tracking experiments are carried out using the three combined models.[Results]The results show that in Trajectory 6,the root mean square error(RMSE)of the position information of the SCKF is smaller than that of the EKF,and the accuracy is improved by 30.06%when Combined Model 1 is used to track the trajectory with varying heading,heading rate and velocity;and when using Combined Model 3,the SCKF has the smaller fluctuation range of RMSE compared to the position information using the EKF in Trajectory 6,and the error value is reduced by 60.80%.Combined Model 3 has the best performance,but its computation is large.In a complex trajectory experiment at constant velocity,the performance of Combined Model 2 is close to that of Combined Model 3.[Conclusions]The proposed method can improve the accuracy of AIS data and ensure the stability of AIS data error fluctuation,making it possible to improve ship motion tracking and monitoring.
作者
杨家轩
陈柏果
马令琪
YANG Jiaxuan;CHEN Baiguo;MA Lingqi(Navigation College,Dalian Maritime University,Dalian 116026,China;Key Laboratory of Navigation Safety Guarantee of Liaoning Province,Dalian 116026,China)
出处
《中国舰船研究》
CSCD
北大核心
2022年第4期12-23,共12页
Chinese Journal of Ship Research
基金
中央高校基本科研业务费专项资金资助(017210127)。
关键词
交互式多模型
平方根容积卡尔曼滤波器
自动识别系统
轨迹跟踪
interactive multi-model(IMM)
square-root cubature Kalman filter(SCKF)
automatic identification system(AIS)
trajectory tracking