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 ...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.展开更多
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].
文摘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.
