基于改进CKF算法的AUV组合导航系统研究  

Research on AUV integrated navigation system based on improved CKF algorithm

作　　者：张晓林 汪俊 严天宏 张昕[2] 何波[2] ZHANG Xiaolin;WANG Jun;YAN Tianhong;ZHANG Xin;HE Bo(School of Mechanical and Electrical Engineer,China Jiliang University,Hangzhou 310018,China;School of Information Science and Engineering,Ocean University of China,Qingdao 266100,China)

机构地区：[1]中国计量大学机电工程学院,浙江杭州310018 [2]中国海洋大学信息科学与工程学部,山东青岛266100

出　　处：《舰船科学技术》2025年第5期37-42,共6页Ship Science and Technology

基　　金：浙江省自然科学基金资助项目(LTGG23E090002)。

摘　　要：针对自主无人水下航行器(AUV)组合导航系统在导航推算时系统模型模糊及测量噪声无法确定导致导航精度下降的问题,提出通过自适应因子调整先验估计误差协方差矩阵的自适应容积卡尔曼滤波(ACKF),以及基于M估计在线调整量测噪声协方差矩阵的鲁棒容积卡尔曼滤波(RCKF),并利用交互式多模型(IMM)将以上优化算法交互融合。结合各个子滤波器的优势,通过设置仿真与实际海试对比实验证明算法的可行性,其中误差降低了29%,均方根误差降低了43%,从而可通过该方法降低AUV导航过程中不同噪声不确定性造成的影响。Addressing the issues of navigation accuracy degradation in autonomous underwater vehicles(AUVs)due to system model uncertainties and indeterminate measurement noise during navigation calculations in the integrated navigation system,this study proposes two filtering methods.Firstly,an adaptive cubature kalman filter(ACKF)is introduced,which adjusts the prior estimation error covariance matrix through adaptive factors.Secondly,a robust cubature kalman filter(RCKF)is presented,which online tunes the measurement noise covariance matrix based on M-estimation.These optimized algorithms are then interactively fused using an interactive multiple model(IMM)approach.Combining the advantages of each sub-filter,the feasibility of the algorithm was demonstrated through a comparison of simulation and actual sea trials.The results showed a 29%reduction in errors and a 43%decrease in root mean square error.Consequently,this method can mitigate the impact of uncertainties caused by various noises during AUV navigation.

关 键 词：自主无人水下航行器 组合导航 卡尔曼滤波 交互式多模型 

分 类 号：U675.73[交通运输工程—船舶及航道工程]