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Research on Ship Track and Navigation Behavior Characteristics
Based on Deep Learning
Yue Yang, Tianyi Liu and Xiaolong Wu
Dalian Naval Academy, Liaoning, China
Keywords: Neural Network, Deep Learning, Ship Track Characteristics, AIS System.
Abstract: Along with the expansion of China's Marine interests and the improvement of comprehensive national
strength, Marine navigation safety and ship abnormal navigation behaviour problems are increasingly acute.
In this paper, a deep learning CALS algorithm based on neural network is proposed to analyse more than
AIS ship sailing track data in the Chinese sea area in 2021, and build ship sailing track prediction and early
warning models. By exploring the navigation characteristics of various types of ships in the Chinese sea area
and the characteristics of navigation behaviour at different time and space scales, this paper is to solve the
problem of abnormal trajectory detection and early warning of ships, and provide information support for safe
navigation and military mission decision-making.
1 INTRODUCTION
With the advent of the era of intelligent big data, the
statistical analysis and mining technology of big data
play an important role. With the continuous
advancement of Chinese-style modernization in the
process of building a maritime power and the
deepening application of Automatic Identification
System (AIS) in maritime safety and communication
between ships and ports, ships and ships, etc., The
historical accumulation of ship track time series data
in the China Sea area has exploded, which provides
strong data support for mining and analyzing the
distribution characteristics of various ship tracks and
the characteristics of navigation behavior at different
time and space scales.
With the continuous progress of China's
comprehensive national strength and the continuous
expansion of overseas interests, the mission of the
People's Navy has gradually changed from offshore
defense to far sea defense. In order to better explore
the navigation characteristics of different types of
ships and navigation behavior characteristics at
different time and space scales in the sea of China,
and make use of the above characteristics to support
military mission decision-making and ensure
navigation safety, this paper starts from the analysis
of ship navigation trajectory and ship behavior. It uses
AIS data of all ships in the sea of China in 2021.
Based on LSTM long and short-term memory
network machine learning algorithm, the innovative
neural network structure uses the real-time sailing
trajectory data of nearby ships to analyze the
abnormal sailing position, tracking anomaly, heading
anomaly, speed anomaly and other abnormal
situations. The new algorithm desire to screen out the
ships that exceed the historical path range and may
have suspicious sailing behaviors. So, it can provide
early warning for military mission decision-making
and safe navigation.
2 DATA PROCESSING
In this paper, about
AIS data of 13 types of
vessels, such as fishing vessels and pilot vessels,
under 11 conditions such as sailing and losing control
in the Chinese sea area in 2021 are collected. After
decoding the original AIS message information, two
tables of Pos and ShipInfo are obtained,
corresponding to ship dynamic information and static
information respectively. The generation of ship
navigation trajectory adopts the method of connecting
ship dynamic information in AIS message
information to a single ship according to the time
series, and records the real-time transmission of
navigation-related information.
370
Yang, Y., Liu, T. and Wu, X.
Research on Ship Track and Navigation Behavior Characteristics Based on Deep Learning.
DOI: 10.5220/0012284200003807
Paper published under CC license (CC BY-NC-ND 4.0)
In Proceedings of the 2nd International Seminar on Artificial Intelligence, Networking and Information Technology (ANIT 2023), pages 370-374
ISBN: 978-989-758-677-4
Proceedings Copyright © 2024 by SCITEPRESS – Science and Technology Publications, Lda.