基于改进多目标粒子群优化算法的主动声呐浮标布阵优化

doi:10.12382/bgxb.2024.0897

摘要/Abstract

摘要：

主动声呐浮标作为反潜机广泛使用的探潜设备,研究其布阵优化方法对快速有效地完成反潜作战任务、提升反潜作战的效率有着重要意义。针对现有研究中潜艇位置散布模型与主动声呐浮标搜潜范围简化,难以契合实际场景需求的问题,建立应召搜潜场景下潜艇在概略航向已知和速度未知条件下的散布规律模型与基于主动声呐方程和网格法的主动声呐浮标阵列搜潜效率评定模型;通过引入Kent映射、动态调整的惯性权重和学习因子,提出基于改进多目标粒子群优化算法的浮标布阵优化方法,并在应召搜潜场景下进行仿真验证。仿真结果表明,在不同场景下所提算法均能得到最优部署方案,验证了新算法的可行性和有效性;与其他算法对比,在相同投放量下新算法搜潜概率更大,求解时间更短,证明了新算法的优越性。

关键词: 主动声呐浮标, 布阵优化, 潜艇, 多目标粒子群优化算法, 反潜

Abstract:

Active sonar buoy is widely used as a submarine detection equipment for anti-submarine aircraft.It is of great significance to study its deployment optimization method to quickly and effectively complete the anti-submarine tasks and improve the efficiency of anti-submarine combat.The problem in current research lies in the fact that the submarine position distribution model and the simplified search range of active sonar buoys for detecting the submarines are difficult to meet the needs of the actual scenario.A distribution law model of submarines under the conditions of known general heading and unknown speed and an evaluation model of active sonar buoy array search efficiency based on the active sonar equation and the grid method are established.A buoy array deployment optimization method based on improved multi-objective particle swarm optimization algorithm is proposed by introducing Kent mapping,dynamically adjusted inertia weight and learning factor.The simulation verification is carried out in the scene of on-call submarine search.The simulated results show that the proposed algorithm can be used to obtain the optimal deployment scheme in different scenarios,which proves its feasibility and effectiveness.Compared with other algorithms,the proposed algorithm has a larger search probability and a shorter computation time under the condition of the same delivery amount,which proves its superiority.

Key words: active sonar buoy, array deployment optimization, submarine, multi-objective particle swarm optimization, anti-submarine

中图分类号:

TJ301

毕文豪, 吴宇轩, 许洋, 张安. 基于改进多目标粒子群优化算法的主动声呐浮标布阵优化[J]. 兵工学报, 2025, 46(9): 240897-.

BI Wenhao, WU Yuxuan, XU Yang, ZHANG An. Optimization of Active Sonar Buoy Array Deployment Based on Improved Multi-objective Particle Swarm Optimization Algorithm[J]. Acta Armamentarii, 2025, 46(9): 240897-.

图/表 19

图1 应召搜潜任务示意图

Fig.1 Schematic diagram of an underwater search mission

图2 潜艇机动后的位置散布概率分布

Fig.2 Position dispersion probability distribution of submarine after maneuvering

图3 潜艇目标强度变化

Fig.3 TS changes of target submarine

图4 网格法求解单枚声呐浮标搜潜效率示意图

Fig.4 Utilizing the grid method to enhance the search efficiency of a single sonar buoy for submariners

图5 算法流程

Fig.5 Algorithm flowchart

表1 仿真参数设置

Table 1 Setting of simulation parameters

参数	数值
r_d/km	10.55
v_a/kn	3
σ₀/km	10
σ_α/(°)	20
M	10
t_n/h	2

表2 算法参数设置

Table 2 Setting of algorithm parameters

参数	数值
popnum	200
k_max	200
ω_max	0.9
ω_min	0.4
c_max	2.0
c_min	0.2
β	0.7

图6 不同主动声呐浮标投放量下搜潜概率最小值、均值和最大值

Fig.6 The minimum,average and maximum search probabilities of different numbers of sonar buoy

表3 不同投放量下的最优解

Table 3 Optimal solutions for different drop quantities

m	1	2	3	4	5
P_s	0.2268	0.4143	0.5401	0.6215	0.6965
m	6	7	8	9	10
P_s	0.7606	0.8078	0.8446	0.8675	0.8952

表4 最优方案投放点坐标

Table 4 The coordinates of the optimal scheme delivery points

序号	(x,y)/km
1	(3.53,32.33)
2	(7.13,17.28)
3	(-4.97,20.97)
4	(-9.20,27.18)
5	(-0.79,14.51)
6	(6.81,24.96)

图7 主动声呐浮标的投放点示意图

Fig.7 Schematic diagram of the drop point of active sonat buoys

表5 最优方案的对潜搜索概率

Table 5 The search probability of optimal scheme

概率	t_j/h
概率	0.5	1.0	1.5	2.0
P_tj	0.6998	0.7415	0.7739	0.7606
P_m/%	69.98	92.24	98.25	99.58

图8 不同时刻部署方案搜潜效果示意图

Fig.8 Submarine search effect diagram of different deployment schemes at different times

图9 不同潜艇初始散布与声呐浮标投放量、搜潜概率的关系

Fig.9 The relationship among the initial dispersion of different submarines and the deployment amount of active sonar buoys and the search probability

图10 不同潜艇初始位置散布对于潜艇机动一段时间后的位置分布的影响示意图

Fig.10 Schematic diagram of the influences of different initial position distributions on the position distributions of submarines after a period of maneuvering

图11 不同概略航向与主动声呐浮标投放量、搜潜概率的关系

Fig.11 The relationship among different approximate courses and active sonar buoy drop amount and search probability

图12 不同潜艇概略航向对于潜艇机动一段时间后的位置分布的影响示意图

Fig.12 Schematic diagram of the influences of different submarine general courses on the position distribution of submarine after a period of maneuvering

图13 当投放量m=6时NSGA-II与改进的MOPSO算法和原MOPSO算法搜潜概率变化

Fig.13 Search probability changes of NSGA-II,the improved MOPSO algorithm and the original MOPSO algorithm for m=6

图14 不同投放量下NSGA-II与改进的MOPSO算法和原MOPSO算法最大搜潜概率与优化时间变化

Fig.14 The maximum search probability and optimization time curves of NSGA-II,the improved MOPSO algorithm and the original MOPSO algorithm under the condition of different delivery amounts

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