Detecting anomalous deviations from standard maritime routes using the Ornstein-Uhlenbeck process
Open accessAbstract
A novel anomaly detection procedure based on the Ornstein–Uhlenbeck (OU) mean-reverting stochastic process is presented. The considered anomaly is a vessel that deviates from a planned route, changing its nominal velocity$\boldsymbol{v}_0$. In order to hide this behavior, the vessel switches off its automatic identification system (AIS) device for a time$T$and then tries to revert to the previous nominal velocity$\boldsymbol{v}_0$. The decision that has to be made is declaring that a deviation either happened or not, relying only upon two consecutive AIS contacts. Furthermore, the extension to the scenario in which multiple contacts (e.g., radar) are available during the time period$T$is also considered. A proper statistical hypothesis testing procedure that builds on the changes in the OU process long-term velocity parameter$\boldsymbol{v}_0$of the vessel is the core of the proposed approach and enables the solution of the anomaly detection problem. Closed analytical forms are provided for the detection and false alarm probabilities of the hypothesis test.
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This line of research has focused on the development of anomaly detection strategies that provide the ability to reveal vessel deviations from standard navigational routes as well as dark deviations in the presence of possibly intentional surveillance gaps.
“This line of research has focused on the development of anomaly detection strategies that provide the ability to reveal vessel deviations from standard navigational routes as well as dark deviations in the presence of possibly intentional surveillance gaps (due to AIS disablements or limited sensor coverage).”
From Space-Based Global Maritime Surveillance. Part II: Artificial Intelligence and Data Fusion Techniques (2021) · cites this paper as [31]
An innovative statistical framework combines available context data with a parametric model of the vessel's kinematic behaviour.
“More recently, an innovative statistical framework [31], [32] that combines the available context data with a parametric model of the vessel's kinematic behaviour has received special attention.”
From Space-Based Global Maritime Surveillance. Part II: Artificial Intelligence and Data Fusion Techniques (2021) · cites this paper as [31]
The anomaly detector in [31], [32] runs a generalized likelihood ratio test to make decisions on the existence of anomalous deviations within a given time window relying upon available measurements such as AIS, radar, or SAR.
“Using the OU model to represent the vessel dynamics and contextual information to define the nominal behaviour at sea, the anomaly detector in [31], [32] runs a hypothesis testing procedure - the generalized likelihood ratio test - to make decisions on the existence of anomalous deviations within a given time window relying upon the available measurements (e.g. AIS, radar, SAR).”
From Space-Based Global Maritime Surveillance. Part II: Artificial Intelligence and Data Fusion Techniques (2021) · cites this paper as [31]
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Elements of Large-Sample Theory
(1999) · Springer Texts in Statistics · doi:10.1007/b98855 · Cited by 677 · see citing works
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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 · see citing works
“More recently, an innovative statistical framework[32], [33] that combines the available context data with a parametric model of the vessel’s kinematic behavior has received special attention.”
BackgroundMethodSelf-citationGamage, C., Dinalankara, R., Samarabandu, J., & Subasinghe, A. (2023) · WMU Journal of Maritime Affairs · doi:10.1007/s13437-023-00312-7 · Cited by 44 · see citing works
Anomaly Detection and Tracking Based on Mean–Reverting Processes with Unknown Parameters
Forti, N., Millefiori, L. M., Braca, P., & Willett, P. (2019) · ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) · doi:10.1109/icassp.2019.8683428 · Cited by 29 · see citing works
Self-citation
- Authors
- d'Afflisio, E., Braca, P., Millefiori, L. M., & Willett, P.
- Affiliations
- North Atlantic Treaty Organization (BE)
- University of Connecticut (US)
- Venue
- IEEE Transactions on Signal Processing
- Published
- 2018-12-15
- Volume
- 66
- Issue
- 24
- Pages
- 6474-6487
- ISSN
- 1053-587X (ISSN-L)
- 1941-0476
- OpenAlex
- W2897838656
- Cited by
- 59
- References
- 1
- Citations by year
- 2026: 5 · 2025: 7 · 2024: 7 · 2023: 8 · 2022: 8 · 2021: 11 · 2020: 6 · 2019: 6 · 2018: 1
- FWCI
- 3.36
- Type
- journal article
- Language
- en
- Open access
- green
- ieeexplore.ieee.org
- Locations
- IEEE Transactions on Signal Processing (publishedVersion)
- Florence Research (University of Florence) (submittedVersion, other-oa)
- Funders
- NATO Allied Command Transformation via the project Data Knowledge Operational Effectiveness at the NATO Science and Technology Organization Centre for Maritime Research and Experimentation
- NATO Allied Command Transformation via the project “Data Knowledge Operational Effectiveness.”
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Anomaly Detection Techniques and Applications
- Physical Sciences › Engineering › Ocean Engineering › Maritime Navigation and Safety
- Physical Sciences › Computer Science › Artificial Intelligence › Target Tracking and Data Fusion in Sensor Networks
- Keywords
- maritime anomaly detection, Ornstein-Uhlenbeck process, Automatic Identification System, hypothesis testing, false alarm probability, detection probability, radar data fusion
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