Study on the data filling model for the turning motion of the training boat with missing observations

In order to process the missing turning motion observations of training boat, a data filling model scheme based on the combination of ARIMA (Autoregressive integrated moving average model) and Kalman filtering was proposed in this paper. Firstly, ARIMA was modelled to forecast the missing course observations. Then, the ARIMA model was converted to state space model, and Kalman filtering was employed to recursively estimate the state of yaw acceleration provided the prediction accuracy is unsatisfactory. Subsequently, fuzzy rules were designed to optimize the missing observations of course to prevent errors caused by extrapolating from different initial values. Finally, the optimized series of course were utilized to estimate the missing ship positions in the polar coordinate mode. A case study of the training boat turning motion at sea verifies the effectiveness of proposed scheme, which is beneficial for the usages of sampling data and the reduction cost of ship trials.