Data-driven Autonomous Decision-making Method for the Effective Position of AUV Torpedo Attacks

GUO Li-qiang; MA Liang; ZHANG Hui; YANG Jing

doi:10.11993/j.issn.2096-3920.202108009

Volume 30 Issue 4

Sep 2022

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Article Navigation > Journal of Unmanned Undersea Systems > 2022 > 30(4): 528-534

GUO Li-qiang, MA Liang, ZHANG Hui, YANG Jing. Data-driven Autonomous Decision-making Method for the Effective Position of AUV Torpedo Attacks[J]. Journal of Unmanned Undersea Systems, 2022, 30(4): 528-534. doi: 10.11993/j.issn.2096-3920.202108009

Citation:

GUO Li-qiang, MA Liang, ZHANG Hui, YANG Jing. Data-driven Autonomous Decision-making Method for the Effective Position of AUV Torpedo Attacks[J]. Journal of Unmanned Undersea Systems, 2022, 30(4): 528-534. doi: 10.11993/j.issn.2096-3920.202108009

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Data-driven Autonomous Decision-making Method for the Effective Position of AUV Torpedo Attacks

doi: 10.11993/j.issn.2096-3920.202108009

Navy Submarine Academy, Qingdao 266199, China

Received Date: 2021-08-23
Rev Recd Date: 2021-10-14

Available Online: 2022-06-27

Abstract

Abstract

Autonomous decision-making capability is a distinctive feature that distinguishes unmanned undersea vehicles from manned platforms. This capability is characterized by a short autonomous decision-making time, high decision-making accuracy, and executable decision-making solutions. In this study, an autonomous decision-making method that combines operational simulation with ensemble learning was proposed to overcome the shortcomings of traditional effective position decision-making methods with regard to the attack effect and decision speed when an autonomous undersea vehicle(AUV) launches an attack against a surface ship using an acoustic homing torpedo. First, many basic experimental data sets were obtained by optimizing the probability of acoustic homing torpedo detection targets. Subsequently, a detection probability attack judgment threshold was designed to convert the AUV’s effective position decision-making into a binary classification problem and form the classification experimental data. Finally, the classification performance of the support vector machine, random forest, and XGBoost were analyzed and it was concluded that ensemble learning is more suitable for this unbalanced sample classification problem. Furthermore, the adaptability of this model under multiple task thresholds and its generalization ability in complex marine environments were tested. The test results showed that this method can meet the AUV’s autonomous attack decision-making requirements and significantly accelerate the decision-making speed while ensuring the effectiveness of the torpedo attack. Therefore, this study provides a reference for research on the attack planning module of equipment.
- autonomous undersea vehicle,
- autonomous decision-making capability,
- ensemble learning,
- torpedo

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References(22)

References

[1]	陈强. 水下无人航行器[M]. 北京: 国防工业出版社, 2014.
[2]	钟宏伟. 国外无人水下航行器装备与技术现状及展望[J]. 水下无人系统学报, 2017, 25(3): 215-225. Zhong Hong-wei. Review and Prospect of Equipment and Techniques for Unmanned Undersea Vehicle in Foreign Countries[J]. Journal of Unmanned Undersea Systems, 2017, 25(3): 215-225.
[3]	孟庆玉, 张静远, 王鹏, 等. 鱼雷作战效能分析[M]. 北京: 国防工业出版社, 2003.
[4]	陈强. 水下无人系统及其装备发展论证[M]. 北京: 国防工业出版社, 2018.
[5]	严代彪, 王树宗. 潜艇发射鱼雷的可攻性问题解析法[J]. 火力与指挥控制, 2004, 29(z1): 15-16. doi: 10.3969/j.issn.1002-0640.2004.z1.006 Yan Dai-biao, Wang Shu-zong. Analytic Arithmetic of the Assaultable Problem of Submarine Shooting Torpedo[J]. Fire Control & Command Control, 2004, 29(z1): 15-16. doi: 10.3969/j.issn.1002-0640.2004.z1.006
[6]	李冬梅, 王树宗. 潜艇发射鱼雷的可攻性问题研究[J]. 舰船科学技术, 2004, 26(4): 54-56. Li Dong-mei, Wang Shu-zong. A Study on the Assaultable Problem of Submarine Torpedoing[J]. Ship Science and Technology, 2004, 26(4): 54-56.
[7]	吴晓海, 周智超, 杜海. 潜艇鱼雷攻击可攻区域[J]. 火力与指挥控制, 2009, 34(4): 131-133. doi: 10.3969/j.issn.1002-0640.2009.04.037 Wu Xiao-hai, Zhou Zhi-chao, Du Hai. Research of the Available Attack Area of Torpedo Launched by Submarine[J]. Fire Control & Command Control, 2009, 34(4): 131-133. doi: 10.3969/j.issn.1002-0640.2009.04.037
[8]	刘勇, 李本昌, 张靖康. 基于鱼雷航程实时预报的潜射线导鱼雷可攻性判断[J]. 指挥控制与仿真, 2009, 31(3): 49-52, 71. doi: 10.3969/j.issn.1673-3819.2009.03.014 Liu yong, Li Ben-chang, Zhang Jing-kang. Attacked Decision-making of Submarine Wire-guided Torpedo Based on Real-time Forecast of Torpedo Range[J]. Command Control& Simulation, 2009, 31(3): 49-52, 71. doi: 10.3969/j.issn.1673-3819.2009.03.014
[9]	孙华春, 张会, 李长文. 声自导鱼雷射击的有利提前角优化模型[J]. 舰船电子工程, 2011, 31(8): 40-42,51. doi: 10.3969/j.issn.1627-9730.2011.08.011 Sun Hua-chun, Zhang Hui, Li Chang-wen. Optimization of Lead Angle for Acoustic Homing Torpedo[J]. Ship Electronic Engineering, 2011, 31(8): 40-42,51. doi: 10.3969/j.issn.1627-9730.2011.08.011
[10]	贾跃, 宋保维, 赵向涛, 等. 水面舰船对声自导鱼雷防御机动方法研究[J]. 火力与指挥控制, 2009, 34(1): 45-48. doi: 10.3969/j.issn.1002-0640.2009.01.013 Jia Yue, Song Bao-wei, Zhao Xiang-tao, et al. A Study on Vessel Evading Method to Acoustic Homing Torpedo[J]. Fire Control and Command Control, 2009, 34(1): 45-48. doi: 10.3969/j.issn.1002-0640.2009.01.013
[11]	崔滋刚, 张仪, 李志伟. 潜射鱼雷攻击水面舰船时的声自导发现概率仿真研究[J]. 计算机测量与控制, 2017, 25(6): 92-94, 119. Cui Zi-gang, Zhang Yi, Li Wei. Simulation Study of Surface Warship Evading Acoustic Homing Torpedo Based on Monte Carlo[J]. Computer Measurement & Control, 2017, 25(6): 92-94, 119.
[12]	卢孟维, 马峰, 魏继锋, 等. 舰船规避对追踪段鱼雷弹道散布规律的影响[J]. 鱼雷技术, 2015, 23(5): 379-383. Lu Meng-wei, Ma Feng, Wei Ji-feng, et al. Influence of Vessel Evasion on the Dispersion Law of Torpedo Trajectory in Tracking Section[J]. Torpedo Technology, 2015, 23(5): 379-383.
[13]	周志华. 机器学习[M]. 北京: 清华大学出版社.
[14]	Chen T, Guestrin C. Xgboost: A Scalable Tree Boosting System[C]//Proceedings of the 22nd ACM Sigkdd International Conference on Knowledge Discovery and Data Mining. California, USA: ACM, 2016: 785-794.
[15]	冷菲, 李巍. 基于XGBoost对肺鳞癌和肺腺癌的分类预测[J]. 首都医科大学学报, 2019, 40(6): 799-893. Leng Fei, Li Wei. Classification Prediction of Lung Squamous Cell Carcinoma and Lung Adenocarcinoma Based on XGBoost[J]. Journal of Capital Medical University, 2019, 40(6): 799-893.
[16]	岳鹏, 侯凌燕, 杨大利, 等. 基于XGBoost特征选择的疾病诊断XLC-Stacking方法[J]. 计算机工程与应用, 2020, 56(17): 136-141. Yue Peng, Hou Ling-yan, Yang Da-li, et al. XLC-Stacking Method for Disease Diagnosis Based on XGBoost Feature Selection[J]. Computer Engineering and Applications, 2020, 56(17): 136-141.
[17]	姜少飞, 邬天骥, 彭翔, 等. 基于XGBoost特征提取的数据驱动故障诊断方法[J]. 中国机械工程, 2020, 31(10): 1232-1239. doi: 10.3969/j.issn.1004-132X.2020.10.015 Jiang Shao-fei, Wu Tian-ji, Peng Xiang, et al. Data Driven Fault Diagnosis Method Based on XGBoost Feature Extraction[J]. China Mechanical Engineering, 2020, 31(10): 1232-1239. doi: 10.3969/j.issn.1004-132X.2020.10.015
[18]	王雅琳, 郭佳, 刘都群. 2018年水下无人系统发展综述[J]. 无人系统技术, 2019, 2(4): 20-25. Wang Ya-lin, Guo Jia, Liu Du-qun. Summary of the Development of Unmanned Undersea Systems in 2018[J]. Unmanned Systems Technology, 2019, 2(4): 20-25.
[19]	陈颜辉. 水面舰艇防御鱼雷原理与应用[M]. 北京: 国防工业出版社, 2015.
[20]	吴鹏, 张会, 张文玉. 潜艇安全走出水面搜索带条件分析和方案优化方法[J]. 舰船科学技术, 2012, 34(6): 95-97. doi: 10.3404/j.issn.1672-7649.2012.06.023 Wu Peng, Zhang Hui, Zhang Wen-yu. The Condition for Submarine Exit Ship’s Search Zone Safely and the Methods for Exit Scheme Optimization[J]. Ship Science and Technology, 2012, 34(6): 95-97. doi: 10.3404/j.issn.1672-7649.2012.06.023
[21]	李浩, 朱焱. 基于梯度分布调节策略的Xgboost算法优化[J]. 计算机应用, 2020, 40(6): 1633-1637. Li Hao, Zhu Yan. Xgboost Algorithm Optimization Based on Gradient Distribution Harmonized Strategy[J]. Journal of Computer Applications, 2020, 40(6): 1633-1637.
[22]	Akiba T, Sano S, Yanase T, et al. Optuna: A Next-Generation Hyperparameter Optimization FrameWork[C]//The 25th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. Anchorage AK USA: Association for Computing Machinery, 2019: 2623-2631.