AUV航迹追踪灰色关联度UKF算法
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摘要
标准的UKF算法是一种高效的线性化航迹追踪方法 ,算法冗余度,且具有较理想的追踪效果。但UKF控制算法当中的采样间隔常被设置为常数,将会影响导航追踪结果的精度。由此,本文提出了应用于AUV航迹追踪的灰色关联度无损卡尔曼滤波算法(即GUKF),用于改善AUV航迹追踪的预测精度。在标准UKF算法的基础上,通过设计一些的判断与反馈机制[1],调整UKF算法每一步的采样间隔t,从而实现系统的采样间隔的自适应变化。通过仿真与结果对比,验证了之前的设想。应用于AUV航迹追踪技术的GUKF算法与标准的UKF算法相比,具有更理想的航迹预测误差精度和鲁棒性。
The standard UKF algorithm is an efficient linear tracking method, algorithm redundancy, and has a better tracking effect.However, the sampling interval in the UKF control algorithm is often set as a constant, which affects the accuracy of the navigation tracking results. Therefore, this paper presents a gray relational non-destructive Kalman filter algorithm(GUKF)applied to AUV track tracking, which is used to improve the prediction accuracy of AUV track tracking. Based on the standard UKF algorithm, the sampling intervalt of each step of the UKF algorithm is adjusted by designing some judgment and feedback mechanisms[1] so as to realize the adaptive change of the sampling intervalt of the system. Through the simulation and the results of comparison, verify the previous idea. Compared with the standard UKF algorithm, the GUKF algorithm applied to AUV trajectory tracking technology has better accuracy and robustness of trajectory prediction error.
The standard UKF algorithm is an efficient linear tracking method, algorithm redundancy, and has a better tracking effect.However, the sampling interval in the UKF control algorithm is often set as a constant, which affects the accuracy of the navigation tracking results. Therefore, this paper presents a gray relational non-destructive Kalman filter algorithm(GUKF)applied to AUV track tracking, which is used to improve the prediction accuracy of AUV track tracking. Based on the standard UKF algorithm, the sampling intervalt of each step of the UKF algorithm is adjusted by designing some judgment and feedback mechanisms[1] so as to realize the adaptive change of the sampling intervalt of the system. Through the simulation and the results of comparison, verify the previous idea. Compared with the standard UKF algorithm, the GUKF algorithm applied to AUV trajectory tracking technology has better accuracy and robustness of trajectory prediction error.
引文
[1]C.E.RASMUSSEN,C.K.I.WILLIAMS.Gaussian Processes for Machine Learning[M].London:The MITPress,2006:207-220.
[2]B.ALLOTTA,A.CAITI,L.CHISCI,et al.Development of a Navigation Algorithm for Autonomous Underwater Vehicles[J].IFAC-PapersOnLine,2015,48(2):64-69.
[3]J.N.NEWMAN.Marine Hydrodynamics[M].London,UK:MIT Press,1999.
[4]O.M.FALTINSEN.Sea loads on ships and offshore structures[M].Cambridge,UK:Cambridge university Press,1990.
[5]J.M.J.JOURN旻E,JAKOB PINKSTER.Introduction in ship hydromechanics[M].Mekelweg,Netherlands:Delft University of Technology Press,2002.
[6]贾鹤鸣,宋文龙,周佳加.基于非线性反步法的欠驱动AUV地形跟踪控制[J].北京工业大学学报,2012,38(12):1780-1785.
[7]李婷,赵德鑫,黄芝平等.一种基于灰色粒子滤波算法的机动AUV航深内测方法[J].国防科技大学学报,2013,35(5):185-190.
[8]BENEDETTO ALLOTTA,ANDREA CAITI,RICCARDO COSTANZI,et al.Development and Online Validation of an UKF-based Navigation Algorithm for AUVs[J].IFAC-Papers On Line,2016,49(15):69-74.
[9]KEISUKE MIYABAYASHI,OSAMU TONOMURA,MANABU KANO,ET AL.Comparative Study of State Estimation of Tubular Microreactors using UKFand EKF[C]//8th IFAC Symposium on Advanced Control of Chemical Processes.Singapore:IFAC,2012:513-518.
[10]LIN XIAO,JEROME JOUFFROY.Modeling and Nonlinear Heading Control of Sailing Yachts[J].IEEEJournal of Oceanic Engineering,2014,39(2):256-268.
[11]ALEXANDER V.Inzartsev.Underwater Vehicles[M].Vienna,Austria:InTech Press,2009:539-556.
[12]田民,刘思峰,卜志坤.灰色关联度算法模型的研究综述[J].统计与决策,2008,2008(1):24-27.
[13]孙芳芳.浅议灰色关联度分析方法及其应用[J].科技信息,2010,2(17):886-888.
[2]B.ALLOTTA,A.CAITI,L.CHISCI,et al.Development of a Navigation Algorithm for Autonomous Underwater Vehicles[J].IFAC-PapersOnLine,2015,48(2):64-69.
[3]J.N.NEWMAN.Marine Hydrodynamics[M].London,UK:MIT Press,1999.
[4]O.M.FALTINSEN.Sea loads on ships and offshore structures[M].Cambridge,UK:Cambridge university Press,1990.
[5]J.M.J.JOURN旻E,JAKOB PINKSTER.Introduction in ship hydromechanics[M].Mekelweg,Netherlands:Delft University of Technology Press,2002.
[6]贾鹤鸣,宋文龙,周佳加.基于非线性反步法的欠驱动AUV地形跟踪控制[J].北京工业大学学报,2012,38(12):1780-1785.
[7]李婷,赵德鑫,黄芝平等.一种基于灰色粒子滤波算法的机动AUV航深内测方法[J].国防科技大学学报,2013,35(5):185-190.
[8]BENEDETTO ALLOTTA,ANDREA CAITI,RICCARDO COSTANZI,et al.Development and Online Validation of an UKF-based Navigation Algorithm for AUVs[J].IFAC-Papers On Line,2016,49(15):69-74.
[9]KEISUKE MIYABAYASHI,OSAMU TONOMURA,MANABU KANO,ET AL.Comparative Study of State Estimation of Tubular Microreactors using UKFand EKF[C]//8th IFAC Symposium on Advanced Control of Chemical Processes.Singapore:IFAC,2012:513-518.
[10]LIN XIAO,JEROME JOUFFROY.Modeling and Nonlinear Heading Control of Sailing Yachts[J].IEEEJournal of Oceanic Engineering,2014,39(2):256-268.
[11]ALEXANDER V.Inzartsev.Underwater Vehicles[M].Vienna,Austria:InTech Press,2009:539-556.
[12]田民,刘思峰,卜志坤.灰色关联度算法模型的研究综述[J].统计与决策,2008,2008(1):24-27.
[13]孙芳芳.浅议灰色关联度分析方法及其应用[J].科技信息,2010,2(17):886-888.