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Ship Spatiotemporal Key Feature Point Online Extraction Based on AIS Multi-Sensor Data Using an Improved Sliding

Miao Gao^1,2,3, Guo-You Shi^4,5,6

¹Navigation College, Dalian Maritime University, Dalian 116026, China. gaomiao4566@foxmail.com.

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|June 19, 2019

Summary

This study introduces an efficient algorithm for extracting key feature points from Automatic Identification System (AIS) data. This method enhances ship navigation behavior analysis and big data mining by improving learning efficiency.

Keywords:

AIS big data mining feature online extraction key feature point ship behavior sliding window algorithm spatiotemporal

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Area of Science:

Maritime technology
Data science
Naval architecture

Background:

Automatic Identification System (AIS) data offers vast potential for ship data mining and navigation behavior analysis.
Large volumes of raw AIS data present challenges in processing, storage, and analysis, leading to low learning efficiency.
Extracting Key Feature Points (KFPs) from ship trajectories is crucial for effective navigation behavior analysis and big data mining.

Purpose of the Study:

To propose a novel algorithm for online extraction of spatiotemporal Key Feature Points (KFPs) from Automatic Identification System (AIS) trajectory data.
To enhance the efficiency and accuracy of ship navigation behavior analysis and big data mining.
To provide a practical method for identifying significant points in ship trajectories.

Main Methods:

A modified sliding window algorithm is developed to analyze ship navigation angle deviation, position deviation, and spatiotemporal characteristics.
The algorithm is applied online to AIS trajectory data for real-time KFP extraction.
Recommended threshold ranges for key parameters are discussed to facilitate algorithm application.

Main Results:

The proposed improved sliding window algorithm effectively extracts KFPs from AIS trajectory data with high accuracy and operational efficiency.
The method demonstrates superior performance compared to the traditional Douglas-Peucker (DP) algorithm.
The algorithm enables rapid and easy KFP extraction, significantly benefiting ship traffic flow and navigational behavior learning.

Conclusions:

The developed spatiotemporal KFP online extraction algorithm offers a significant advancement for processing large-scale AIS data.
This approach improves the efficiency and effectiveness of ship navigation behavior analysis and big data mining.
The method provides a valuable tool for understanding and optimizing ship traffic and navigation patterns.