Finding a needle in a haystack: Satellite detection of moving objects in marine environments

Natalie Fridman; Doron Amir; Han Schvartzman; Oded Stawitzky; Igor Kleinerman; Sharon Kligsberg; Noa Agmon

Finding a needle in a haystack: Satellite detection of moving objects in marine environments

Natalie Fridman, Doron Amir, Han Schvartzman, Oded Stawitzky, Igor Kleinerman, Sharon Kligsberg, Noa Agmon

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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
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

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

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 14 Life Below Water

All Science Journal Classification (ASJC) codes

Software
Artificial Intelligence
Control and Systems Engineering

Cite this

Fridman, N., Amir, D., Schvartzman, H., Stawitzky, O., Kleinerman, I., Kligsberg, S., & Agmon, N. (2017). Finding a needle in a haystack: Satellite detection of moving objects in marine environments. In E. Durfee, M. Winikoff, K. Larson, & S. Das (Eds.), 16th International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2017 (pp. 1541-1543). (Proceedings of the International Joint Conference on Autonomous Agents and Multiagent Systems, AAMAS; Vol. 3).

Fridman, Natalie ; Amir, Doron ; Schvartzman, Han et al. / Finding a needle in a haystack : Satellite detection of moving objects in marine environments. 16th International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2017. editor / Edmund Durfee ; Michael Winikoff ; Kate Larson ; Sanmay Das. 2017. pp. 1541-1543 (Proceedings of the International Joint Conference on Autonomous Agents and Multiagent Systems, AAMAS).

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title = "Finding a needle in a haystack: Satellite detection of moving objects in marine environments",

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.",

author = "Natalie Fridman and Doron Amir and Han Schvartzman and Oded Stawitzky and Igor Kleinerman and Sharon Kligsberg and Noa Agmon",

note = "Publisher Copyright: {\textcopyright} Copyright 2017, International Foundation for Autonomous Agents and Multiagent Systems (www.ifaamas.org). All rights reserved.; 16th International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2017 ; Conference date: 08-05-2017 Through 12-05-2017",

year = "2017",

language = "الإنجليزيّة",

series = "Proceedings of the International Joint Conference on Autonomous Agents and Multiagent Systems, AAMAS",

pages = "1541--1543",

editor = "Edmund Durfee and Michael Winikoff and Kate Larson and Sanmay Das",

booktitle = "16th International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2017",

}

Fridman, N, Amir, D, Schvartzman, H, Stawitzky, O, Kleinerman, I, Kligsberg, S & Agmon, N 2017, Finding a needle in a haystack: Satellite detection of moving objects in marine environments. in E Durfee, M Winikoff, K Larson & S Das (eds), 16th International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2017. Proceedings of the International Joint Conference on Autonomous Agents and Multiagent Systems, AAMAS, vol. 3, pp. 1541-1543, 16th International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2017, Sao Paulo, Brazil, 8/05/17.

Finding a needle in a haystack: Satellite detection of moving objects in marine environments. / Fridman, Natalie; Amir, Doron; Schvartzman, Han et al.
16th International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2017. ed. / Edmund Durfee; Michael Winikoff; Kate Larson; Sanmay Das. 2017. p. 1541-1543 (Proceedings of the International Joint Conference on Autonomous Agents and Multiagent Systems, AAMAS; Vol. 3).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Finding a needle in a haystack

T2 - 16th International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2017

AU - Fridman, Natalie

AU - Amir, Doron

AU - Schvartzman, Han

AU - Stawitzky, Oded

AU - Kleinerman, Igor

AU - Kligsberg, Sharon

AU - Agmon, Noa

PY - 2017

Y1 - 2017

N2 - 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.

AB - 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.

UR - http://www.scopus.com/inward/record.url?scp=85046478524&partnerID=8YFLogxK

M3 - منشور من مؤتمر

T3 - Proceedings of the International Joint Conference on Autonomous Agents and Multiagent Systems, AAMAS

SP - 1541

EP - 1543

BT - 16th International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2017

A2 - Durfee, Edmund

A2 - Winikoff, Michael

A2 - Larson, Kate

A2 - Das, Sanmay

Y2 - 8 May 2017 through 12 May 2017

ER -

Fridman N, Amir D, Schvartzman H, Stawitzky O, Kleinerman I, Kligsberg S et al. Finding a needle in a haystack: Satellite detection of moving objects in marine environments. In Durfee E, Winikoff M, Larson K, Das S, editors, 16th International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2017. 2017. p. 1541-1543. (Proceedings of the International Joint Conference on Autonomous Agents and Multiagent Systems, AAMAS).

Finding a needle in a haystack: Satellite detection of moving objects in marine environments

Abstract

Publication series

Conference

UN SDGs

All Science Journal Classification (ASJC) codes

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Cite this