Unsupervised maritime anomaly detection for intelligent situational awareness using AIS data

With the mandatory implementation of the automatic identification system and the rapid advancement of relevant satellite communication technologies, a vast amount of vessel trajectory data has been amassed. It has catalyzed advancements in the field of maritime anomaly detection, significantly contributing to the enhancement of intelligent maritime situational awareness. However, detecting abnormal vessel trajectories from massive data is a highly challenging task that requires extensive manual effort. To address the challenge, this paper develops an unsupervised deep learning method for detecting abnormal vessel trajectories. Specifically, we first generate the informative trajectory images by remapping the raw vessel trajectories into two-dimensional matrices while maintaining the spatio-temporal properties. Then, a Wasserstein generative adversarial network with gradient penalty (WGAN-GP) is trained on normal vessel trajectories. Next, an encoder is trained to map these trajectory images to a latent space. Finally, the trained encoder and WGANGP are used to design an anomaly score for detecting abnormal vessel trajectories and anomalies in vessel trajectories. Experimental evaluations using multiple evaluation metrics on diverse simulated and real-world maritime environments validate the efficacy of the proposed method in unsupervised maritime anomaly detection. The results indicate that the proposed model can provide accurate anomaly detection on vessel trajectories without manual labeling.