基于组合赋权TOPSIS的智能UUV目标识别与反对抗效能研究

王旭; 李金明; 毛昭勇; 丁文俊

doi:10.11993/j.issn.2096-3920.2023-0094

基于组合赋权TOPSIS的智能UUV目标识别与反对抗效能研究

doi: 10.11993/j.issn.2096-3920.2023-0094

王旭¹,
李金明²,
毛昭勇^{3, 5, 6},
丁文俊^{3, 4, 5, 6}

1.
西北工业大学航海学院, 陕西西安, 710072
2.
中国船舶集团有限公司第七〇五研究所, 陕西西安, 710077
3.
西北工业大学无人系统技术研究院, 陕西西安, 710072
4.
浙江西安交通大学研究院, 浙江杭州, 311200
5.
无人飞行器技术全国重点实验室, 陕西西安, 710072
6.
无人机技术集成攻关大平台, 陕西西安, 710072

基金项目: 国家自然科学基金项目(51909206); 中国博士后科学基金项目(2021M692616); 陕西省自然科学基础研究计划项目(2024JC-YBMS-300); 中央高校基本科研业务费专项资金项目(31020200QD044).

详细信息

中图分类号: TJ6
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- HTML全文浏览量: 14
- PDF下载量: 10
- 被引次数: 0
出版历程
- 收稿日期: 2023-07-31
- 修回日期: 2023-10-08
- 录用日期: 2023-11-17
- 网络出版日期: 2024-09-14

Research on Effectiveness Evaluation of Intelligent UUV Target Recognition and Anti-Countermeasure Based on Combination Weighting TOPSIS

WANG Xu¹,
LI Jinming²,
MAO Zhaoyong^{3, 5, 6},
DING Wenjun^{3, 4, 5, 6}

1.
School of Marine Science and Technology, Northwestern Polytechnical University, Xi’an 710072, China
2.
The 705 Research Institute, China State Shipbuilding Corporation Limited, Xi’an 710077, China
3.
Unmanned System Research Institute, Northwestern Polytechnical University, Xi’an 710072, China
4.
Research Institute of Xi’an Jiaotong University, Zhejiang 311215, China
5.
National Key Laboratory of Unmanned Aerial Vehicle Technology, Xi’an 710072, China
6.
Integrated Research and Development Platform of Unmanned Aerial Vehicle Technology, Xi’an 710072, China

摘要

摘要: 随着水声对抗作战环境日益复杂, 未来水下作战的重点主要是智能水下无人系统的对抗与反对抗, 研究智能无人水下航行器(UUV)目标识别与反对抗能力对提高智能水下无人系统的整体作战效能具有重要意义。为了评估智能UUV目标识别与反对抗效能, 文中分析了影响系统效能的主要因素, 建立了系统效能指标体系, 并给出了评估模型; 运用主客观相结合的组合赋权方法确定指标体系的权重, 采用改进的逼近理想点法(TOPSIS)对智能UUV目标识别与反对抗效能进行评估, 评估结论可为UUV目标识别与反对抗系统设计与优化提供参考。
- 智能无人水下航行器 /
- 目标识别 /
- 反对抗 /
- 效能评估 /
- 组合赋权 /
- 逼近理想点法
Abstract: At present, the operational environment for underwater acoustic countermeasures is increasingly complicated, The emphasis of future underwater warfare is mainly focused on the confrontation and anti-countermeasure of intelligent underwater unmanned system. The research on intelligent unmanned undersea vehicle(UUV) recognition and anti-countermeasure capability is of great significance for improving the overall operational effectiveness of intelligent underwater unmanned system. In order to evaluate the target recognition and countermeasure effectiveness of the intelligent UUV, the main factors affecting the system effectiveness are analyzed, the system effectiveness index system is established, and the evaluation model is proposed. In this study, the weight of the index system is determined by the combination of subjective and objective weighting method, and the target recognition and countermeasure effectiveness of the intelligent UUV is evaluated by the improved technique for order preference by similarity to ideal solution(TOPSIS). The evaluation conclusion can provide a reference for the design and optimize of the intelligent UUV target recognition and anti-countermeasure system.
- intelligent UUV /
- target recognition /
- anti-countermeasure /
- effectiveness evaluation /
- combination weighting /
- TOPSIS

HTML全文

图 1 未来典型水下作战行动示意图

Figure 1. Diagram of future typical underwater operations

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图 2 智能UUV目标识别与反对抗流程图

Figure 2. Flow chart of target recognition and anti-countermeasure of intelligent UUV

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图 3 智能UUV目标识别与反对抗效能指标体系

Figure 3. System of target recognition and anti-countermeasure effectiveness index of intelligent UUV

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图 4 UUV导引命中目标示意图

Figure 4. Diagram of UUV guided hit target

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图 5 各指标权重比较

Figure 5. Weight comparison of each indicator

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图 6 效能评估结果

Figure 6. the result of the effectiveness evaluation

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表 1 UUV目标识别与反对抗指标数值表

Table 1. UUV target recognition and anti-countermeasure index numerical table

指标	${Y_1}$	${Y_2}$	${Y_3}$	${Y_4}$
等效搜索范围/m	1848.2	2177.4	1921.7	2283.8
自导作用距离/m	1517.6	1661.6	1347.3	1564.3
探测扇面/(°)	75.0	88.0	75.0	88.0
目标捕获距离/m	1870.4	1715.4	1577.9	1430.8
目标捕获时间/s	83.5	79.6	80.6	62.4
目标捕获概率/(%)	85	86	91	95
UUV航程消耗/km	7.1833	8.3654	5.3256	7.7554
抗脉冲压力值/MPa	3.1	3.7	3.1	3.7
距离误差/m	73.8	82.5	71.8	67.9
航向误差/(°)	6.06	5.6	5.73	5.26
分辨能力	8	10	8	10
有效攻击率/(%)	35.6	40.11	36.5	48.34

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表 2 能力层判断矩阵及权重

Table 2. Ability level judgment matrix and weight

U	U₁	U₂	U₃	${\omega _0}$
U₁	1	3	5	0.636 99
U₂	1/3	1	3	0.258 28
U₃	1/5	1/3	1	0.104 73

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表 3 熵权法权重计算结果

Table 3. Index weight calculation results of entropy weight method

指标	w
等效搜索范围/m	0.062 561
自导作用距离/m	0.046 934
探测扇面/(°)	0.052 802
目标捕获距离/m	0.081 82
目标捕获时间/s	0.119 34
目标捕获概率/(%)	0.016 778
UUV航程消耗/km	0.264 22
抗脉冲压力值/MPa	0.064 644
距离误差/m	0.040 583
航向误差/(°)	0.021 398
分辨能力	0.102 59
有效攻击率/(%)	0.126 34

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表 4 组合赋权计算结果

Table 4. Calculation results of combination weighting

指标	w
等效搜索范围/m	0.086 6
自导作用距离/m	0.218 6
探测扇面/(°)	0.131 7
目标捕获距离/m	0.066 7
目标捕获时间/s	0.065 6
目标捕获概率/(%)	0.108 9
UUV航程消耗/km	0.118 1
抗脉冲压力值/MPa	0.031 6
距离误差/m	0.018 7
航向误差/(°)	0.020 2
分辨能力	0.065 8
有效攻击率/(%)	0.067 7

下载: 导出CSV

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