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Data-Driven Operational Assessment Method and Digital Twin System for Unmanned Surface Vehicles
1
College of Computer and Artificial Intelligence, Beijing Technology and Business University, Beijing 100048, China
2
School of Computing and Artificial Intelligence, Xinjiang Hetian College, Hetian 848000, China
* Corresponding Author:
Yuting Bai,
[email protected]
Volume 2, Issue 1
2026
Received: 21 May 2025
Accepted: 24 December 2025
Published: 08 January 2026
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Article Information
Published in
ICCK Transactions on Machine Intelligence
Volume/Issue
Volume 2, Issue 1, 2026
Pages
38-52
Cited by
3 (Crossref)
2 (Scopus)
Abstract
To address the challenge of effectively leveraging multi-source data for automated operational assessment of Unmanned Surface Vehicles (USVs) and utilizing digital technologies for monitoring and control, this paper proposes a data-driven state assessment method for surface unmanned systems and develops a digital twin system tailored for USVs. First, a dual-channel feature modeling mechanism is constructed by integrating physically interpretable statistical features with temporal convolutional features. Second, a complementary modeling strategy is adopted using CatBoost for static classification and GRU for dynamic modeling, while a Covariance Intersection (CI) fusion strategy is introduced to enhance the classification performance and adaptability of the model. Finally, a digital twin system is designed that incorporates Position Estimation, Attitude Estimation, and State Evaluation, enabling real-time monitoring and multidimensional visualization of USV operational states. Experimental results demonstrate that the proposed method outperforms baseline approaches in terms of accuracy, F1-score, and other key metrics, exhibiting strong generalization capability and promising potential for practical deployment.
Graphical Abstract
Keywords
unmanned surface vehicles
data-driven
state evaluation
digital twin
multi-source temporal modeling
model fusion
Data Availability Statement
Data will be made available on request.
Funding
This work was supported by the Natural Science Foundation of Xinjiang Uygur Autonomous Region under Grant 2024D01A04, and the National Natural Science Foundation of China under Grant 62203020.
Conflicts of Interest
The authors declare no conflicts of interest.
Ethical Approval and Consent to Participate
Not applicable.
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APA Style
Bai, Y., Hu, J., Tursun, E. & Yimit, H. (2026). Data-Driven Operational Assessment Method and Digital Twin System for Unmanned Surface Vehicles. ICCK Transactions on Machine Intelligence, 2(1), 38–52. https://doi.org/10.62762/TMI.2025.444910
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TY - JOUR AU - Bai, Yuting AU - Hu, Jiyuan AU - Tursun, Eziz AU - Yimit, Hurxida PY - 2026 DA - 2026/01/08 TI - Data-Driven Operational Assessment Method and Digital Twin System for Unmanned Surface Vehicles JO - ICCK Transactions on Machine Intelligence T2 - ICCK Transactions on Machine Intelligence JF - ICCK Transactions on Machine Intelligence VL - 2 IS - 1 SP - 38 EP - 52 DO - 10.62762/TMI.2025.444910 UR - https://www.icck.org/article/abs/TMI.2025.444910 KW - unmanned surface vehicles KW - data-driven KW - state evaluation KW - digital twin KW - multi-source temporal modeling KW - model fusion AB - To address the challenge of effectively leveraging multi-source data for automated operational assessment of Unmanned Surface Vehicles (USVs) and utilizing digital technologies for monitoring and control, this paper proposes a data-driven state assessment method for surface unmanned systems and develops a digital twin system tailored for USVs. First, a dual-channel feature modeling mechanism is constructed by integrating physically interpretable statistical features with temporal convolutional features. Second, a complementary modeling strategy is adopted using CatBoost for static classification and GRU for dynamic modeling, while a Covariance Intersection (CI) fusion strategy is introduced to enhance the classification performance and adaptability of the model. Finally, a digital twin system is designed that incorporates Position Estimation, Attitude Estimation, and State Evaluation, enabling real-time monitoring and multidimensional visualization of USV operational states. Experimental results demonstrate that the proposed method outperforms baseline approaches in terms of accuracy, F1-score, and other key metrics, exhibiting strong generalization capability and promising potential for practical deployment. SN - 3068-7403 PB - Institute of Central Computation and Knowledge LA - English ER -
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@article{Bai2026DataDriven,
author = {Yuting Bai and Jiyuan Hu and Eziz Tursun and Hurxida Yimit},
title = {Data-Driven Operational Assessment Method and Digital Twin System for Unmanned Surface Vehicles},
journal = {ICCK Transactions on Machine Intelligence},
year = {2026},
volume = {2},
number = {1},
pages = {38-52},
doi = {10.62762/TMI.2025.444910},
url = {https://www.icck.org/article/abs/TMI.2025.444910},
abstract = {To address the challenge of effectively leveraging multi-source data for automated operational assessment of Unmanned Surface Vehicles (USVs) and utilizing digital technologies for monitoring and control, this paper proposes a data-driven state assessment method for surface unmanned systems and develops a digital twin system tailored for USVs. First, a dual-channel feature modeling mechanism is constructed by integrating physically interpretable statistical features with temporal convolutional features. Second, a complementary modeling strategy is adopted using CatBoost for static classification and GRU for dynamic modeling, while a Covariance Intersection (CI) fusion strategy is introduced to enhance the classification performance and adaptability of the model. Finally, a digital twin system is designed that incorporates Position Estimation, Attitude Estimation, and State Evaluation, enabling real-time monitoring and multidimensional visualization of USV operational states. Experimental results demonstrate that the proposed method outperforms baseline approaches in terms of accuracy, F1-score, and other key metrics, exhibiting strong generalization capability and promising potential for practical deployment.},
keywords = {unmanned surface vehicles, data-driven, state evaluation, digital twin, multi-source temporal modeling, model fusion},
issn = {3068-7403},
publisher = {Institute of Central Computation and Knowledge}
}
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