Vessel Trajectory Prediction Considering Difference Between Heading and Data Changes

Automatic identification system (AIS) data can reflect the specific dynamic of the ship at the current moment in real time, and the existing BP (Back Propagation) neural network based methods for ship trajectory analysis and prediction only take the heading data into the model directly. The methods do not consider the large deviation between the actual direction change range and the data change range when the ship heading changes near zero. In order to solve this problem, a ship AIS trajectory prediction model based on the improved neural network algorithm is constructed in this paper. The model introduces the double trigonometric function transformation on the basis of BP neural network. The sine value and cosine value are included in the model to consider the two-dimension direction of the heading. The inverse trigonometric function transformation and average processing are carried out to postprocess the predicted data. By selecting the case data to verify the model, the case results show that the prediction error of the model is smaller than the method without considering the difference, which greatly reduces the error range and can be more accurate for ship trajectory prediction.