Estimation of buoy drifting based on adaptive parameter-varying time scale Kalman filter
作者机构：School of NavigationJimei UniversityXiamenPeople’s Republic of China National and local joint engineering research center of ship aided navigation technologyJimei UniversityXiamenPeople’s Republic of China Fujian Shipping Research InstituteJimei UniversityXiamenPeople’s Republic of China Xiamen southeast International Shipping Research CenterJimei UniversityXiamenPeople’s Republic of China
出 版 物：《Journal of Control and Decision》 (控制与决策学报（英文）)
年 卷 期：2021年第8卷第3期
页 面：353-362页
核心收录：
基 金：This work was supported in part by National Natural Science Foundation of China[grant number 51579114] Fujian Provincial Natural Science Foundation Projects[grant number 2018J05085] Research and Cultivation Fund for high level subject of transportation engineering of Jimei University[grant number 202003]
主 题：Kalman filter buoy drift state estimation
摘 要：To solve Kalman filter with dynamic time scale problem,an adaptive parameter-varying time scale kalman filter(APVTS-KF)is *** adaptive mechanism for choosing the covariance of state noise is ***-KF is used to estimate the buoy drifting trajectory with different report *** drifting data of four buoys are used to test the proposed *** influence of report interval,drifting distance,adaptive factor and noise covariance are analysed and *** experimental results and error analysis show that APVTS-KF is better than other algorithms in trajectory ***,Kalman filtering can be used for accurate trajectory estimation in the actual situation of buoy drifting with dynamic time intervals.
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