Data-Driven Operational Assessment Method and Digital Twin System for Unmanned Surface Vehicles

Yuting Bai; Jiyuan Hu; Eziz Tursun; Hurxida Yimit

doi:10.62762/TMI.2025.444910

Volume 2, Issue 1, ICCK Transactions on Machine Intelligence

Volume 2, Issue 1, 2026

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ICCK Transactions on Machine Intelligence, Volume 2, Issue 1, 2026: 38-52

Free to Read | Research Article | 08 January 2026

Data-Driven Operational Assessment Method and Digital Twin System for Unmanned Surface Vehicles

Yuting Bai 1 *

Jiyuan Hu 1

Eziz Tursun 2

Hurxida Yimit 2

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]

DOI: 10.62762/TMI.2025.444910

ARK: ark:/57805/tmi.2025.444910

Received: 21 May 2025, Accepted: 24 December 2025, Published: 08 January 2026

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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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