强跟踪自适应CKF及其在动力定位中应用  

Strong tracking adaptive CKF and application for dynamic positioning

作　　者：徐树生[1] 李娟[1] 温利[1] 龚丽农[1] 

机构地区：[1]青岛农业大学机电工程学院,山东青岛266109

出　　处：《电机与控制学报》2015年第2期101-108,共8页Electric Machines and Control

基　　金：国家自然科学基金(61374126);青岛农业大学博士基金(6631430)

摘　　要：针对容积卡尔曼滤波(cubature Kalman filter,CKF)在动力定位系统存在不良测量及船舶运动模型不确定时滤波精确度下降甚至发散的问题,提出一种强跟踪自适应CKF算法。利用新息协方差匹配原理,建立对系统的不良测量具有鲁棒性的自适应CKF。基于强跟踪滤波(strong tracking filter,STF)的理论框架和自适应CKF算法,构建强跟踪自适应CKF,该算法克服了标准强跟踪滤波的理论局限性,兼具STF与自适应CKF的优点,在系统存在模型不确定性及不良测量时具有良好的鲁棒性和滤波精确度。通过仿真验证了所建算法的有效性。To overcome the problem that cubature Kalman filter （CKF） degrade in accuracy,even divergence when vessel dynamic positioning system have abnormal measurements and model uncertainty,a strong tracking adaptive CKF algorithm was proposed.With innovation covariance matching techniques an adaptive CKF was built to insensitive to measurement malfunctions.Strong tracking adaptive CKF views STF as the basic theory framework and makes adaptive CKF replace extended Kalman filter （EKF）.The algorithm has the advantages of STF and adaptive CKF,and it avoids theoretical limitation of STF.In case of model uncertainty of system and measurement malfunctions,the proposed algorithm has strong robustness and high accuracy.Simulation results show the effectiveness of the presented algorithm.

关 键 词：强跟踪滤波器 容积卡尔曼滤波 自适应滤波 鲁棒性 

分 类 号：TP273[自动化与计算机技术—检测技术与自动化装置]