The Vessel route pattern extraction and anomaly detection from AIS data

Download

index.pdf

Date

2019

Author

Boztepe, Gözde

Metadata

Show full item record

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Item Usage Stats

198
views

209
downloads

The need for a variety of auxiliary analytical tools to enhance marine safety and marine status awareness has been expressed by various platforms. There are lots of data sources breaking out while the ship is on cruising. Automatic Identification System (AIS) device that is widely used in vessels, is one of them. It broadcasts information such as type of ship, identity number, state, destination, estimated time of arrival (ETA), location, speed, direction, cargo. In this study, to aid operators while sailing, the trajectory extraction and anomaly detection tool have been developed. The AIS messages are used to improve a system for safe navigation. Three different approaches are applied for the prediction of the vessel trajectories. Later, movements that have not matched the route patterns and unusual stop anomalies have been examined.

Subject Keywords

AIS, trajectory, anomaly, LSTM, vessel, maritime

URI

http://etd.lib.metu.edu.tr/upload/12623897/index.pdf
https://hdl.handle.net/11511/44461

Collections

Graduate School of Natural and Applied Sciences, Thesis

Suggestions

OpenMETU
Core

Trajectory pattern extraction and anomaly detection for maritime vessels Karatas, Gozde Boztepe; Karagöz, Pınar; Ayran, Orhan (2021-12-01) Trajectory analysis and extraction of trajectory patterns are crucial to enhance marine safety and marine status awareness. The major data source for such analysis is Automatic Identification System (AIS), which publishes data related to movement of the ship while cruising. AIS broadcasts information including type of ship, identity number, state, destination, estimated time of arrival (ETA), location, speed, direction, and cargo. In this paper, we focus on extracting a variety of trajectory patterns for ma...
A simulation study of ad hoc networking of UAVs with opportunistic resource utilization networks Lilien, Leszek T.; BEN OTHMANE, Lotfi; Angın, Pelin; DECARLO, Andrew; Salih, Raed M.; BHARGAVA, Bharat (Elsevier BV, 2014-02-01) Specialized ad hoc networks of unmanned aerial vehicles (UAVs) have been playing increasingly important roles in applications for homeland defense and security. Common resource virtualization techniques are mainly designed for stable networks; they fall short in providing optimal performance in more dynamic networks such as mobile ad hoc networks (MANETs)-due to their highly dynamic and unstable nature. We propose application of Opportunistic Resource Utilization Networks (Oppnets), a novel type of MANETs, ...
An analytical approach for modelling unmanned aerial vehicles and base station interaction for disaster recovery scenarios Owilla, Eugene; Ever, Enver; Computer Engineering (2022-8) Unmanned Aerial Vehicles (UAVs) are an emerging technology with the potential to be used in various sectors for a wide array of applications and services. In wireless networking, UAVs are a vital part of supplementary infrastructure aimed at improv- ing coverage principally during public safety crises. Due to their relatively low cost and scalability of use, there has been a mushrooming focus into the roles that UAVs can play in ameliorating service provided to stranded ground devices. Following a public sa...
Deep Distance Metric Learning For Maritime Vessel Identification Gundogdu, Erhan; Solmaz, Berkan; Koç, Aykut; Yucesoy, Veysel; Alatan, Abdullah Aydın (2017-05-18) This paper addresses the problem of maritime vessel identification by exploiting the state-of-the-art techniques of distance metric learning and deep convolutional neural networks since vessels are the key constituents of marine surveillance. In order to increase the performance of visual vessel identification, we propose a joint learning framework which considers a classification and a distance metric learning cost function. The proposed method utilizes the quadruplet samples from a diverse image dataset t...
Detection of mini/micro unmanned air vehicle (UAV) under clutter presence and environmental effects Dere, İsmail Gökhan; Kuzuoğlu, Mustafa; Department of Electrical and Electronics Engineering (2019) Detection, tracking and classification of Unmanned Air Vehicles (UAVs) is an emerging and crucial capability of radars in recent years. In the presence of clutter such as a crowded city or a foggy weather above sea surface, mini and micro UAVs become very difficult for radars to detect, track and classify. Classification information of UAV targets can be very useful for the critical infrastructures in order to provide security. Examined studies imply that kinematic and characteristic features such as Dopple...

Citation Formats

G. Boztepe, “The Vessel route pattern extraction and anomaly detection from AIS data,” Thesis (M.S.) -- Graduate School of Natural and Applied Sciences. Computer Engineering., Middle East Technical University, 2019.