The DOI matches its Crossref record.
Multiple Ornstein–Uhlenbeck Processes for Maritime Traffic Graph Representation
Abstract
We propose an unsupervised procedure to automatically extract a graph-based model of commercial maritime traffic routes from historical Automatic Identification System (AIS) data. In the proposed representation, the main elements of maritime traffic patterns, such as maneuvering regions and sea-lanes, are represented, respectively, with graph vertices and edges. Vessel motion dynamics are defined by multiple Ornstein-Uhlenbeck processes with different long-run mean parameters, which in our approach can be estimated with a change detection procedure based on Page's test, aimed to reveal the spatial points representative of velocity changes. A density-based clustering algorithm is then applied to aggregate the detected changes into groups of similar elements and reject outliers. To validate the proposed graph-based representation of the maritime traffic, two performance criteria are tested against a real-world trajectory dataset collected off the Iberian Coast and the English Channel. Results show the effectiveness of the proposed approach, which is suitable to be integrated at any level of a JDL system.
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In the probabilistic joint anomaly detection and tracking methodology, any anomalous deviation is represented as a switching unknown control input that modifies the nominal object dynamics, i.e., the OU mean velocity parameter.
“The key idea behind this approach is to represent any anomalous deviation as a switching unknown control input that goes into action by modifying the nominal object dynamics, i.e., the OU mean velocity parameter.”
From Space-Based Global Maritime Surveillance. Part II: Artificial Intelligence and Data Fusion Techniques (2021) · cites this paper as [33]
A probabilistic joint anomaly detection and tracking methodology for sequential detection of maritime anomalous deviations and simultaneous vessel tracking allows a mathematical definition of nominal vessel behaviour based on the combination of the OU dynamic model and context information extracted from historical data.
“This method allows for a mathematical definition of nominal vessel behaviour based on the combination of the OU dynamic model and context information extracted from historical data.”
From Space-Based Global Maritime Surveillance. Part II: Artificial Intelligence and Data Fusion Techniques (2021) · cites this paper as [33]
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Cited by
Sorted by citations
A Multi-Task Deep Learning Architecture for Maritime Surveillance Using AIS Data Streams
Nguyen, D., Vadaine, R., Hajduch, G., Garello, R., & Fablet, R. (2018) · 2018 IEEE 5th International Conference on Data Science and Advanced Analytics (DSAA) · doi:10.1109/dsaa.2018.00044 · Cited by 143
Soldi, G., Gaglione, D., Forti, N., Millefiori, L. M., Braca, P., Carniel, S., Simone, A. D., Iodice, A., Riccio, D., Daffina, F. C., et al. (2021) · IEEE Aerospace and Electronic Systems Magazine · doi:10.1109/maes.2021.3070884 · Cited by 44
“Further advancements on vector-based knowledge discovery [11], [12] have recently led to the development of an unsupervised graph-based methodology to identify the spatiotemporal dynamics of ship routes, and efficiently extract a compact representation of global maritime patterns from large volumes of historical AIS d…”
BackgroundMethodSelf-citationd'Afflisio, E., Braca, P., Millefiori, L. M., & Willett, P. (2018) · 2018 21st International Conference on Information Fusion (FUSION) · doi:10.23919/icif.2018.8455854 · Cited by 33
Self-citationHybrid Bernoulli Filtering for Detection and Tracking of Anomalous Path Deviations
Forti, N., Millefiori, L. M., & Braca, P. (2018) · 2018 21st International Conference on Information Fusion (FUSION) · doi:10.23919/icif.2018.8455567 · Cited by 14
Self-citation
Metadata
- Authors
- Coscia, P., Braca, P., Millefiori, L. M., Palmieri, F. A. N., & Willett, P.
- Affiliations
- University of Campania "Luigi Vanvitelli" (IT)
- NATO Centre for Maritime Research and Experimentation (IT)
- University of Connecticut (US)
- Venue
- IEEE Transactions on Aerospace and Electronic Systems
- Published
- 2018-10
- Volume
- 54
- Issue
- 5
- Pages
- 2158-2170
- ISSN
- 0018-9251 (ISSN-L)
- 1557-9603
- 2371-9877
- OpenAlex
- W2790115023
- Cited by
- 77
- References
- 0
- Citations by year
- 2026: 3 · 2025: 8 · 2024: 10 · 2023: 6 · 2022: 10 · 2021: 14 · 2020: 14 · 2019: 4 · 2018: 7
- FWCI
- 7.81
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- IEEE Transactions on Aerospace and Electronic Systems (publishedVersion)
- CINECA IRIS Institutial research information system (University of Pisa) (submittedVersion)
- Archivio Istituzionale della Ricerca - Università degli Studi della Campania "Luigi Vanvitelli" (submittedVersion)
- Padua Research Archive (University of Padova) (submittedVersion)
- Funders
- NATO Allied Command Transformation
- NATO Science and Technology Organization
- Centre for Maritime Research and Experimentation
- NPS via ONR Contract (N00244-16-1-0017)
- Topics
- Physical Sciences › Engineering › Ocean Engineering › Maritime Navigation and Safety
- Physical Sciences › Computer Science › Information Systems › Data Management and Algorithms
- Physical Sciences › Engineering › Aerospace Engineering › Air Traffic Management and Optimization
- Keywords
- Ornstein-Uhlenbeck process, ship maneuvering, shipping routes, AIS data, change detection, density-based clustering, outlier removal, unsupervised graph learning, maritime domain awareness, vessel trajectory analysis
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