Abstract
There is a growing need in maritime missions to monitor moving vessels with satellite sensors, in order to detect vessels that may mislead about their identity and transmit wrong identification parameters. In order to provide an efficient and cost-effective solution, vessel behavior prediction is a necessary ability. We present three models for vessel behavior prediction: Min-Max, Uniform-Walk and Normal-Walk. We use real marine traffic data (AIS, Automatic Identification System) to compare the performance of these models and their ability to predict vessel behavior in a time frame of 1-11 hours.
| Original language | English |
|---|---|
| Title of host publication | 16th International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2017 |
| Editors | Edmund Durfee, Michael Winikoff, Kate Larson, Sanmay Das |
| Publisher | International Foundation for Autonomous Agents and Multiagent Systems (IFAAMAS) |
| Pages | 1541-1543 |
| Number of pages | 3 |
| ISBN (Electronic) | 9781510855076 |
| State | Published - 2017 |
| Event | 16th International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2017 - Sao Paulo, Brazil Duration: 8 May 2017 → 12 May 2017 |
Publication series
| Name | Proceedings of the International Joint Conference on Autonomous Agents and Multiagent Systems, AAMAS |
|---|---|
| Volume | 3 |
| ISSN (Print) | 1548-8403 |
| ISSN (Electronic) | 1558-2914 |
Conference
| Conference | 16th International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2017 |
|---|---|
| Country/Territory | Brazil |
| City | Sao Paulo |
| Period | 8/05/17 → 12/05/17 |
Bibliographical note
Publisher Copyright:© Copyright 2017, International Foundation for Autonomous Agents and Multiagent Systems (www.ifaamas.org). All rights reserved.
