A Novel Model for Ship Trajectory Anomaly Detection Based on Gaussian Mixture Variational Autoencoder

The use of traditional methods in anomaly detection of multi-class ship trajectories showed some limitations in terms of robustness and learning ability of trajectory features. In view of this, an anomaly detection model for ship trajectory data based on Gaussian Mixture Variational Autoencoder (GMVAE) is proposed in this study using an unsupervised classification method. The proposed model modifies Variational Autoencoder (VAE) by changing the inferential distribution of prior distribution and approximate posterior to the Gaussian mixture model. A high-dimensional hidden space is constructed to learn the features of multi-class trajectory data, and the Dynamic Time Warping (DTW) method is applied to measure the error between the reconstructed trajectory and the original trajectory in order to judge whether the ship trajectory is abnormal. The Automatic Identification System (AIS) data from the US coastal areas are used to verify the proposed model, and the results are compared with other commonly used models in a manually labeled dataset. The research results indicate that the detection rate of the proposed model is 91.26%, and the false alarm rate is 0.68%, which performs the best. Using the Gaussian mixture model to describe the distribution of hidden space can improve the learning ability of multi-class trajectories of VAE, thus increasing the robustness of the model. This research can provide technical support for ship trajectory data analysis and risk management of maritime transportation.