Ship trajectory prediction model based on GAN and attention mechanism

Abstract

In order to improve the accuracy of ship trajectory prediction in waters with complex traffic conditions such as inland rivers and ports, and solve the limitation of a single LSTM in extracting time series feature information, a ship trajectory prediction model based on generative adversarial network and attention mechanism (AGAN) is proposed. The ship's trajectory is predicted collaboratively, the ability of the model to extract key information in the trajectory is improved through the attention mechanism, the relative motion information between multiple ships is extracted through the pooling layer, the individual information and the global information are fused, and finally the generative adversarial network (GAN) is used. Features that are continuously optimized in adversarial improve the accuracy of the model. The final experimental results show that the ship trajectory prediction model based on generative adversarial network has higher accuracy.