UUV threat assessment method based on EBM

LIU Shuwei; CHENG Jianqing; LIU kai

doi:10.11993/j.issn.2096-3920.2024-0121

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LIU Shuwei, CHENG Jianqing, LIU kai. UUV threat assessment method based on EBM[J]. Journal of Unmanned Undersea Systems. doi: 10.11993/j.issn.2096-3920.2024-0121

Citation:

LIU Shuwei, CHENG Jianqing, LIU kai. UUV threat assessment method based on EBM[J]. Journal of Unmanned Undersea Systems. doi: 10.11993/j.issn.2096-3920.2024-0121

LIU Shuwei, CHENG Jianqing, LIU kai. UUV threat assessment method based on EBM[J]. Journal of Unmanned Undersea Systems. doi: 10.11993/j.issn.2096-3920.2024-0121

Citation:

LIU Shuwei, CHENG Jianqing, LIU kai. UUV threat assessment method based on EBM[J]. Journal of Unmanned Undersea Systems. doi: 10.11993/j.issn.2096-3920.2024-0121

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UUV threat assessment method based on EBM

doi: 10.11993/j.issn.2096-3920.2024-0121

Jiangsu Automation Research Institute, Lianyungang, 222006, China

Received Date: 2024-07-09
Accepted Date: 2024-09-25
Rev Recd Date: 2024-09-21

Available Online: 2024-10-28

Abstract

Abstract

In order to solve the problems of lack of data mining ability and insufficient explanatory nature of neural network algorithms when traditional threat assessment methods process complex battlefield situation data, this paper proposes an innovative solution: unmanned undersea vehicle(UUV) threat assessment model based on Explainable Boosting Machine(EBM). As an advanced machine learning technology, EBM cleverly integrates gradient boosting and generalized additive model to achieve the perfect combination of high interpretability of linear model and accuracy of gradient boosting algorithm. In this study, the performance of the EBM model was comprehensively evaluated and compared with several other mainstream machine learning methods, including CatBoost, AdaBoost, and Deep Learning. Through simulation experiments, we found that the EBM model not only maintained high interpretability, but also showed excellent accuracy, the accuracy of the EBM model reached 98.10% in the identification of threat levels. This result not only verifies the effectiveness of the EBM model in complex battlefield situation analysis, but also provides a solid theoretical foundation and technical support for UUV's autonomous decision-making.
- threat assessment,
- unmanned undersea vehicle,
- large samples,
- explainable boosting machine

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References(18)

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