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Modeling vessel kinematics using a stochastic mean-reverting process for long-term prediction
Abstract
We present a novel method for predicting long-term target states based on mean-reverting stochastic processes. We use the Ornstein-Uhlenbeck (OU) process, leading to a revised target state equation and to a time scaling law for the related uncertainty that in the long term is shown to be orders of magnitude lower than under the nearly constant velocity (NCV) assumption. In support of the proposed model, an analysis of a significant portion of real-world maritime traffic is provided.
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The MTG method builds on recent advances in long-term vessel motion modeling whereby the dynamics of ships can be effectively described by a piecewise Ornstein-Uhlenbeck mean-reverting stochastic process.
“This method builds on recent advances in long-term vessel motion modeling [23], [24] whereby the dynamics of ships can be effectively described by a piecewise Ornstein-Uhlenbeck (OU) mean-reverting stochastic process.”
From Space-Based Global Maritime Surveillance. Part II: Artificial Intelligence and Data Fusion Techniques (2021) · cites this paper as [23]
In this framework, the formulation of maritime anomaly detection is based on the OU dynamic model.
“Unlike other works [18], [28]-[30], here the formulation of maritime anomaly detection is based on the OU dynamic model [23].”
From Space-Based Global Maritime Surveillance. Part II: Artificial Intelligence and Data Fusion Techniques (2021) · cites this paper as [23]
The OU model makes it possible to statistically represent with increased accuracy the dynamics of maritime traffic, associate sparse measurements to tracks, and synthesize historical ship trajectories into a sequence of waypoints connected by a network of navigational legs.
“The OU model makes it possible to statistically represent with increased accuracy the dynamics of maritime traffic, associate sparse (in space and time) measurements to tracks, and hence synthesize historical ship trajectories into a sequence of waypoints connected together by a network of navigational legs (with non-maneuvering motion between waypoints).”
From Space-Based Global Maritime Surveillance. Part II: Artificial Intelligence and Data Fusion Techniques (2021) · cites this paper as [23]
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Research works printed but not linked: 36
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Cited by
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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
“This method builds on recent advances in long-term vessel motion modeling [17], [25] whereby the dynamics of ships can be effectively described by a piecewise Ornstein–Uhlenbeck (OU) mean-reverting stochastic process.”
BackgroundMethodInfluentialSelf-citationGamage, C., Dinalankara, R., Samarabandu, J., & Subasinghe, A. (2023) · WMU Journal of Maritime Affairs · doi:10.1007/s13437-023-00312-7 · Cited by 44
d'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-citationAnomaly 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
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
- Millefiori, L. M., Braca, P., Bryan, K., & Willett, P.
- Affiliations
- NATO Centre for Maritime Research and Experimentation (IT)
- University of Connecticut (US)
- Venue
- IEEE Transactions on Aerospace and Electronic Systems
- Published
- 2016-10
- Volume
- 52
- Issue
- 5
- Pages
- 2313-2330
- ISSN
- 0018-9251 (ISSN-L)
- OpenAlex
- W2572270144
- Cited by
- 119
- References
- 0
- Citations by year
- 2026: 9 · 2025: 10 · 2024: 17 · 2023: 12 · 2022: 13 · 2021: 18 · 2020: 14 · 2019: 11 · 2018: 11 · 2017: 3 · 2016: 1
- FWCI
- 7.14
- 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)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Target Tracking and Data Fusion in Sensor Networks
- Physical Sciences › Engineering › Ocean Engineering › Maritime Navigation and Safety
- Physical Sciences › Engineering › Aerospace Engineering › Radar Systems and Signal Processing
- Keywords
- Ornstein-Uhlenbeck process, shipping activity, uncertainty modeling, ship trajectory prediction
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