Sensor Allocation and Online-Learning-Based Path Planning for Maritime Situational Awareness Enhancement: A Multi-Agent Approach-Reference-Cited by-同舟云学术

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Sensor Allocation and Online-Learning-Based Path Planning for Maritime Situational Awareness Enhancement: A Multi-Agent Approach

Published:2024-09 Issue:9 Volume:25 Page:11635-11647
ISSN:1524-9050
Container-title:IEEE Transactions on Intelligent Transportation Systems
language:
Short-container-title:IEEE Trans. Intell. Transport. Syst.

Author:

Nguyen Bach Long¹^ORCID,Doan Anh-Dzung²,Chin Tat-Jun²^ORCID,Guettier Christophe³,Gupta Surabhi⁴,Parra Estelle³,Reid Ian⁵,Wagner Markus¹

Affiliation:

1. Department of Data Science and AI, Monash University, Clayton, VIC, Australia

2. Australian Institute for Machine Learning, The University of Adelaide, Adelaide, SA, Australia

3. Safran Electronics & Defense, Massy, France

4. Safran Electronics and Defense Australasia, Botany, NSW, Australia

5. Department of Computer Vision, Mohamed bin Zayed University of Artificial Intelligence, Masdar City, Abu Dhabi, United Arab Emirates

Funder

Australian Research Council

Safran Electronics and Defense Australasia

Safran Electronics and Defense

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx7/6979/10659279/10443856.pdf?arnumber=10443856

Reference51 articles.

1. Massive Maritime Path Planning: A Contextual Online Learning Approach

2. Maritime Environment Perception Based on Deep Learning

3. Dynamic Radar Network of UAVs: A Joint Navigation and Tracking Approach

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