基于AUV的航迹追踪自适应UKF算法
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摘要
无迹卡尔曼滤波算法(UKF,unscented kalman filter)是一种常见的(AUV,autonomous underwater vehicle)加权统计线性回归航迹追踪算法,其算法冗余度低于(EKF,extended kalman filter)、(PF,particle filter)及(PSO,particle swarm optimization)等数值优化算法,且算法效率较高。然而,UKF控制算法中的系统采样时间间隔通常会被设置为常数,由此可能会产生预测值的误差累积,从而影响导航预测结果的精度。因此,笔者提出了基于AUV的航迹追踪自适应无迹卡尔曼滤波算法(AUKF,adaptive unscented kalamn filter algorithm),以期降低预测算法的累积误差。该预测方法依据标准UKF算法的原理,通过构造相应的约束、判断与反馈机制,调整系统状态方程中每一步的采样间隔t,从而提升算法的航迹追踪精度并减少过程噪声及传感器噪声对预测过程的影响。最后,通过仿真实验与结果对比,近一步验证了之前所提出的设想。
The unscented Kalman filter(UKF)is a common weighted statistical linear regression AUV tracking algorithm.Its computational redundancy is lower than those of numerical optimization algorithms such as EKF,PF and PSO,and the tracking efficiency is high.However,the systematic sampling interval in the UKF control method is usually set to a constant value,which may result in the accumulation of errors in the predicted values and influence the accuracy of the navigation prediction results.Therefore,an adaptive unscented Kalman filter algorithm(AUKF)based on AUV tracking is proposed in this paper to reduce the cumulative error of the predicted algorithm.Based on the standard UKF algorithm principle,this prediction method adjusts the sampling interval t of each step in the system state function through the construction of corresponding constraint,judgment and feedback mechanism,so as to improve the tracking accuracy of the algorithm and reduce the influence of the process noise and sensor noise on the prediction process.By simulation experiment and the result comparison,the idea put forward earlier is verified.
The unscented Kalman filter(UKF)is a common weighted statistical linear regression AUV tracking algorithm.Its computational redundancy is lower than those of numerical optimization algorithms such as EKF,PF and PSO,and the tracking efficiency is high.However,the systematic sampling interval in the UKF control method is usually set to a constant value,which may result in the accumulation of errors in the predicted values and influence the accuracy of the navigation prediction results.Therefore,an adaptive unscented Kalman filter algorithm(AUKF)based on AUV tracking is proposed in this paper to reduce the cumulative error of the predicted algorithm.Based on the standard UKF algorithm principle,this prediction method adjusts the sampling interval t of each step in the system state function through the construction of corresponding constraint,judgment and feedback mechanism,so as to improve the tracking accuracy of the algorithm and reduce the influence of the process noise and sensor noise on the prediction process.By simulation experiment and the result comparison,the idea put forward earlier is verified.
引文
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[15]韩涵,王厚军,龙兵,等.基于改进马氏距离的模拟电路故障诊断方法[J].控制与决策,2013,28(11):1713-1717.HAN Han,WANG Houjun,LONG Bing,et al.Method for analog circuit fault diagnosis based on improved mahalanobis distance[J].Control and Decision,2013,28(11):1713-1717.(in Chinese)
[2]Rasmussen C E,Williams C K I.Gaussian processes for machine learning[M].London:MIT Press,2006:207-220.
[3]姚绪梁,杨光仪,彭宇.水下自主航行器垂直面运动的预测控制[J].哈尔滨工业大学学报,2017,46(9):166-173.YAO Xuliang,YANG Guangyi,PENG Yu.Predictive control for diving of an autonomous underwater vehicle[J].Journal of Harbin Institute of Technology,2017,46(9):166-173.(in Chinese)
[4]Kundu P K,Cohen I M.Fluid mechanics[M].Burlington,USA:Elsevier Press,2008.
[5]Journée J M J,Jakob P.Introduction in ship hydromechanics[M].Mekelweg,Netherlands:Delft University of Technology Press,2002.
[6]王艳艳,刘开周,封锡盛.AUV纯方位目标跟踪轨迹优化方法[J].机器人,2014,36(2):179-184.WANG Yanyan,LIU Kaizhou,FENG Xisheng.Optimal AUV trajectories for bearings-only tracking[J].Robot,2014,36(2):179-184.(in Chinese)
[7]Allotta B,Caiti A,Costanzi R,et al.Development and online validation of an UKF-based navigation algorithm for AUVs[J].IFAC-Papers OnLine,2016,49(15):69-74.
[8]Xiao L,Jouffroy J.Modeling and nonlinear heading control of sailing yachts[J].IEEE Journal of Oceanic Engineering,2014,39(2):256-268.
[9]Inzartsev A V.Underwater vehicles[M].Vienna,Austria:Intech Press,2009:539-556.
[10]Miyabayashi K,Tonomura O,Kano M,et al.Comparative study of state estimation of tubular microreactors using UKFand EKF[J].IFAC Proceedings Volumes,2012,45(15):513-518.
[11]刘斌,马晓川,侯朝焕.针对高速自治水下航行器的UKF主动目标跟踪算法[J].系统仿真学报,2008,20(4):947-950.LIU Bin,MA Xiaochuan,HOU Chaohuan.A UKF-based active target tracking algorithm for high-speed autonomous underwater vehicles[J].Journal of System Simulation,2008,20(4):947-950.(in Chinese)
[12]李小龙,段凤阳,许金凯,等.高机动条件下组合导航自适应滤波算法研究及改进[J].长春理工大学学报(自然科学版),2010,33(3):48-51.LI Xiaolong,DUAN Fengyang,XU Jinkai,et al.Study and improvement on adaptive filter of integrated navigation system in highly dynamic environment[J].Journal of Changchun University of Science and Technology(Natural Science Edition),2010,33(3):48-51.(in Chinese)
[13]梅江元.基于马氏距离的度量学习算法研究及应用[D].哈尔滨:哈尔滨工业大学,2016.MEI Jiangyuan.Research on mahalanobis distance based metric learning algorithm and its applications[D].Haerbin:Harbin Institute of Technology,2016.(in Chinese)
[14]吴香华,牛生杰,吴诚鸥,等.马氏距离聚类分析中协方差矩阵估算的改进[J].数理统计与管理,2011,30(2):240-245.WU Xianghua,NIU Shengjie,WU Chengou,et al.An improvement on estimating covariance matrix during cluster analysis using mahalanobis distance[J].Application of Statistics and Management,2011,30(2):240-245.(in Chinese)
[15]韩涵,王厚军,龙兵,等.基于改进马氏距离的模拟电路故障诊断方法[J].控制与决策,2013,28(11):1713-1717.HAN Han,WANG Houjun,LONG Bing,et al.Method for analog circuit fault diagnosis based on improved mahalanobis distance[J].Control and Decision,2013,28(11):1713-1717.(in Chinese)