Finding Probability of Submarine-Launched Acoustic Homing Torpedo Based on Gaussian Process Regression

REN Bin; XIE Chao

doi:10.11993/j.issn.2096-3920.2023-0113

Article Contents

Article Navigation > Journal of Unmanned Undersea Systems > 2024 > Accepted Manuscript

REN Bin, XIE Chao. Finding Probability of Submarine-Launched Acoustic Homing Torpedo Based on Gaussian Process Regression[J]. Journal of Unmanned Undersea Systems. doi: 10.11993/j.issn.2096-3920.2023-0113

Citation:

REN Bin, XIE Chao. Finding Probability of Submarine-Launched Acoustic Homing Torpedo Based on Gaussian Process Regression[J]. Journal of Unmanned Undersea Systems. doi: 10.11993/j.issn.2096-3920.2023-0113

Citation:

PDF( 1401 KB)

Finding Probability of Submarine-Launched Acoustic Homing Torpedo Based on Gaussian Process Regression

doi: 10.11993/j.issn.2096-3920.2023-0113

REN Bin¹,
XIE Chao²

1.
Marine Equipment Project Management Center, Beijing 100071, China
2.
Beijing Institute of Technology, School of Automation, Beijing 100081, China

Received Date: 2023-09-28
Accepted Date: 2023-11-22
Rev Recd Date: 2023-10-30

Available Online: 2024-03-25

Abstract

Abstract

The determination of the probability associated with submarine-launched acoustic homing torpedoes significantly influences tactical formulation. Conventional analytical and statistical algorithms encounter challenges in balancing the trade-off between the speed and precision of probability assessment. In response to this issue, this paper introduces a novel estimation model for assessing the probability of detection based on Gaussian process regression. Additionally, a methodology is proposed for generating training data using an analytical model. Subsequently, numerical simulations were conducted within a specific battlefield scenario. The outcomes illustrate the superior performance of the proposed method, offering valuable theoretical insights for informed decision-making in relevant battlefield contexts.
- submarine-launched acoustic torpedo,
- finding probability,
- Gaussian process regression

FullText(HTML)

References(10)

References

[1]	孟庆玉, 张静远, 宋保维. 鱼雷作战效能分析[M]. 北京: 国防工业出版社, 2003.
[2]	杨绪升, 刘建兵, 周庆飞. 声自导鱼雷射击诸元及误差对其捕获概率的影响[J]. 指挥控制与仿真, 2009, 31(5): 93-97. doi: 10.3969/j.issn.1673-3819.2009.05.024 Yang Xusheng, Liu Jianbing, Zhou Qingfei. Effect on acoustic homing torpedo capture probability of fire elements and errors[J]. Command Control & Simulation, 2009, 31(5): 93-97. doi: 10.3969/j.issn.1673-3819.2009.05.024
[3]	薄玉成. 武器系统设计理论[M]. 北京: 北京理工大学出版社, 2010.
[4]	谢超, 周景军, 万亚民, 等. 基于脱靶量散布的超空泡鱼雷命中概率研究[J]. 水下无人系统学报, 2022, 30(2): 237-244,253. doi: 10.11993/j.issn.2096-3920.2022.02.015 Xie Chao, Zhou Jingjun, Wan Yamin, et al. Research on hitting probability of supercavitating torpedo based on dispersion of miss distance[J]. Journal of Unmanned Undersea Systems, 2022, 30(2): 237-244,253. doi: 10.11993/j.issn.2096-3920.2022.02.015
[5]	谢超, 周景军, 万亚民, 等. 超空泡鱼雷命中概率解析方法[J]. 水下无人系统学报, 2022, 30(5): 656-664. doi: 10.11993/j.issn.2096-3920.202111002 Xie Chao, Zhou Jingjun, Wan Yamin, et al. Analytical method for hitting probability of supercavitating torpedo[J]. Journal of Unmanned Undersea Systems, 2022, 30(5): 656-664. doi: 10.11993/j.issn.2096-3920.202111002
[6]	李勐, 代志恒. 直航鱼雷发现概率的解析计算方法[J]. 指挥控制与仿真, 2007, 39(4): 55-59. Li Meng, Dai Zhiheng. Analytic method for calculating probability of direct torpedo[J]. Commend Control & Simulation, 2007, 39(4): 55-59.
[7]	武志东, 于雪泳, 许兆鹏. 潜射鱼雷命中概率的解析计算通式及应用[J]. 水下无人系统学报, 2021, 29(2): 203-209. Wu Zhidong, Yu Xueyong, Xu Zhaopeng. Analytic formula and employment of the hitting probability for sub-launched torpedo[J]. Journal of Unmanned Undersea Systems, 2021, 29(2): 203-209.
[8]	胡星志, 王旭, 江雄, 等. 基于高斯过程回归的高超声速飞行器不确定轨迹预测[J]. 空天技术, 2022(4): 49-61. Hu Xingzhi, Wang Xu, Jiang Xiong, et al. Uncertain trajectory prediction of hypersonic flight vehicles based on Gaussian process regression[J]. Aerospace Technology, 2022(4): 49-61.
[9]	Quinonero-Candela J, Rasmussen C E. A unifying view of sparse approximate Gaussian process regression[J]. The Journal of Machine Learning Research, 2005, 6: 1939-1959.
[10]	Schulz E, Speekenbrink M, Krause A. A tutorial on Gaussian process regression: Modelling, exploring, and exploiting functions[J]. Journal of Mathematical Psychology, 2018, 85: 1-16. doi: 10.1016/j.jmp.2018.03.001