Maritime Anomaly Detection within Coastal Waters Based on Vessel Trajectory Clustering and Naïve Bayes Classifier
Maritime Anomaly Detection within Coastal Waters Based on Vessel Trajectory Clustering and Naïve Bayes Classifier
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DOI：
10.1017/s0373463316000850
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发表时间：
2017-01
影响因子：
2.4
通讯作者：
Rong Zhen;Yongxing Jin;Q. Hu;Zheping Shao;N. Nikitakos
中科院分区：
文献类型：
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作者：
Rong Zhen;Yongxing Jin;Q. Hu;Zheping Shao;N. Nikitakos
海上异常检测是智能船舶交通监控系统和实现海上态势感知的关键技术。本文提出了一种结合船舶轨迹聚类和Naïve贝叶斯分类器的方法来检测海上监视系统中船舶的异常行为。基于自动识别系统（AIS）数据的空间和方向特征，设计了船舶轨迹之间的相似性度量方法，然后应用层次聚类和k-介质聚类方法对港口水域内典型船舶航行模式进行建模和学习。建立了Naïve容器行为贝叶斯分类器，对异常容器行为进行分类和检测。该方法已在厦门湾和中国城三角水域的AIS数据上进行了船舶轨迹测试和验证。结果表明，该方法是有效的，有助于提高近海海域的海上态势感知能力。
Maritime anomaly detection is a key technique in intelligent vessel traffic surveillance systems and implementation of maritime situational awareness. In this paper, we propose a method which combines vessel trajectory clustering and Naïve Bayes classifier to detect anomalous vessel behaviour in the maritime surveillance system. A similarity measurement between vessel trajectories is designed based on the spatial and directional characteristics of Automatic Identification System (AIS) data, then the method of hierarchical and k-medoids clustering are applied to model and learn the typical vessel sailing pattern within harbour waters. The Naïve Bayes classifier of vessel behaviour is built to classify and detect anomalous vessel behaviour. The proposed method has been tested and validated on the vessel trajectories from AIS data within the waters of Xiamen Bay and Chengsanjiao, China. The results indicate that the proposed method is effective and helpful, thus enhancing maritime situational awareness in coastal waters.