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Automatic Identification System Data Fusion
Lie Yang 1, Shao-Hua Hsu 2, Zhi-Jie Yu 3 and Yung-Cheng Yao 4
1Department of Electrical Engineering, National Kaohsiung University of Science and Technology, Kaohsiung, Taiwan
2Department of Marine Leisure Management, National Kaohsiung University of Science and Technology, Kaohsiung, Taiwan
3Auray Technology Corp, Kaohsiung, Taiwan
4Department of Microelectronics Engineering, National Kaohsiung University of Science and Technology, Kaohsiung, Taiwan
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Abstract:
With the rise of intelligent computing applications such as artificial intelligence and big data analytics, the quality of data sources has become crucial for accurate decision-making. This is primarily because data collected from various sources needs to be organized and processed to extract information for analysis and subsequent decision-making. With advancements in communication technology and the growth of maritime transportation, the Automatic Identification System (AIS) has become a fundamental communication device for maritime vessel navigation. Due to the extensive range of data provided by the AIS and its diverse functional requirements, there may be instances of incomplete or duplicated data packets during the transmission process to shore-based stations, caused by signal interference or poor reception. This study, with the assistance of the National Academy of Marine Research (2024) which provided AIS data sources, focuses on establishing a data management mechanism for the AIS database. Additionally, it involves the development of AIS data processing algorithms for tasks such as data format parsing, field interpretation, and cleaning. The aim of the study is to establish an operational and standardized AIS data quality control mechanism that effectively identifies and eliminates abnormal or inconsistent AIS data within the system. By doing so, the study seeks to maintain the data stability and reliability of the AIS database while providing high-quality AIS data.
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Keywords: Automatic Identification System (AIS), Data Quality, AIS Database, Data Processing.
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*Corresponding author; e-mail: yuchyao@nkust.edu.tw
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©
2024
Marine Research , ISSN 2709-6629
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