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

作　　者：Guanlin Wu Haipeng Wang Yu Liu You He 

机构地区：[1]Shenzhen International Graduate School,Tsinghua University,Shenzhen,518071,China [2]Institute of Space Science and Applied Technology,Harbin Institute of Technology,Shenzhen,518055,China [3]Department of Electronic Engineering,Tsinghua University,Beijing,100084,China

出　　处：《Digital Communications and Networks》2024年第3期620-630,共11页数字通信与网络（英文版）

基　　金：supported by National Natural Science Foundation of China under Grants No.62076249,62022092,62293545.

摘　　要：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 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 Spatial-Temporal Generative Adversarial Network(GraphSTGAN),is proposed in this paper,which uses graph neural network to model unstructured data and uses STGAN to learn the spatial-temporal dependencies and interactions.Extensive experiments show the effectiveness and robustness of the proposed method.

关 键 词：Internet of things MULTI-AGENTS Graph neural network Maritime monitoring services 

分 类 号：TN929.5[电子电信—通信与信息系统]