GraphSTGAN: Situation understanding network of slow-fast high maneuvering targets for maritime monitor services of IoT data

With the rapid growth of the maritime internet of things (IoT) devices for maritime monitor services (MMS), maritime traffic controllers could not handle a massive amount of data in time. For unmanned MMS, one of the key technologies is situation understanding. However, the complex motion of sea surface targets, the presence of slow-fast high maneuvering targets and track breakages due to radar blind zones make modeling the dynamics of marine multi-agents difficult, and pose significant challenges to maritime situation understanding. In order to comprehend the situation accurately and thus offer unmanned MMS, it is crucial to model the complex dynamics of multi-agents using IoT big data. Nevertheless, previous methods typically rely on complex assumptions, are plagued by unstructured data, and disregard the interactions between multiple agents and the spatial-temporal correlations. A deep learning model-Graph neural Spatial-Temporal GAN (GraphSTGAN) Network is proposed in this paper, which uses graph neural network to model unstructured data and uses STGAN to learn the spatial-temporal dependencies and the interaction. Extensive experiments show the effectiveness and robustness of the proposed method.