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Abstract
To solve Kalman filter with dynamic time scale problem, an adaptive parameter-varying time scale kalman filter (APVTS-KF) is designed. An adaptive mechanism for choosing the covariance of state noise is designed. APVTS-KF is used to estimate the buoy drifting trajectory with different report intervals. Position drifting data of four buoys are used to test the proposed algorithm. The influence of report interval, drifting distance, adaptive factor and noise covariance are analysed and compared. The experimental results and error analysis show that APVTS-KF is better than other algorithms in trajectory estimation. Thus, Kalman filtering can be used for accurate trajectory estimation in the actual situation of buoy drifting with dynamic time intervals.
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Han Xue
Han Xue was born in Xiamen, Fujian in 1982. She received her B.S. degree in University of Science and Technology of China in 2005. She received her PhD degree in National University of Defense Technology in 2010. Since 2014, she has been a lecturer in Institute of Navigation, Jimei University. She is a member of Chinese Association of Automation.
Tian Chai
Tian Chai was born in Fuyang, Anhui, China in 1981. He received his B.S. degree in Jimei University in 2003. He received his Master’s degree in Dalian Maritime University in 2006. He received her PhD degree in Dalian Maritime University in 2018. He is an associate professor in the school of navigation of Jimei University. He achieved the third prize of excellent papers in 2011 Annual Academic Meeting of China Maritime Society.