An Anomaly Detection Method for AIS Trajectory Based on Kinematic Interpolation

With the enormous amount of information provided by the ship Automatic Identification System (AIS), AIS is now playing a significant role in maritime transport system-related research and development. Many kinds of research and industrial applications are based on the ship trajectory extracted from...

Full description

Saved in:
Bibliographic Details
Published inJournal of marine science and engineering Vol. 9; no. 6; p. 609
Main Authors Guo, Shaoqing, Mou, Junmin, Chen, Linying, Chen, Pengfei
Format Journal Article
LanguageEnglish
Published Basel MDPI AG 01.06.2021
Subjects
AIS
Anomalies
anomaly detection
Artificial intelligence
Clustering
Data
Datasets
Detection
Human factors
Industrial applications
Information processing
Interpolation
kinematic interpolation
Kinematics
Knowledge
Marine transportation
Methods
R&D
Research & development
Satellites
ship trajectory
Transportation systems
Online AccessGet full text

Cover

More Information
Summary:With the enormous amount of information provided by the ship Automatic Identification System (AIS), AIS is now playing a significant role in maritime transport system-related research and development. Many kinds of research and industrial applications are based on the ship trajectory extracted from raw AIS data. However, due to the issues of equipment, the transmission environment, and human factors, the raw AIS data inevitably contain abnormal messages, which have hindered the utilization of such information in practice. Thus, in this paper, an anomaly detection method that focuses on AIS trajectory is proposed, making comprehensive use of the kinematic information of the ship in the AIS data. The method employs three steps to obtain non-error AIS trajectories: (1) data preprocessing, (2) kinematic estimation, and (3) error clustering. It should be noted that steps (2) and (3) are involved in an iterative process to determine all of the abnormal data. A case study is then conducted to test the proposed method on real-world AIS data, followed by a comparison between the proposed method and the rule-based anomaly detection method. As the processed trajectories show fewer abnormal features, the results indicate that the method improves performance and can accurately detect as much abnormal data as possible.
ISSN:2077-1312
2077-1312
DOI:10.3390/jmse9060609