1. 北京理工大学 爆炸科学与安全防护全国重点试验室, 北京 100081
2. 北京理工大学 长三角研究院, 浙江 嘉兴 314000
3. 93756 部队, 天津 300401
4. 32801 部队, 北京 100082
* jasonyan9023@163.com
** liuyan@bit.edu.cn
收稿:2024-09-25,
网络出版:2025-09-24,
纸质出版:2025-09-30
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尹鹏, 黄风雷, 石科仁, 等. 基于改进粒子群优化算法的多弹打击面目标瞄准点优化方法[J]. 兵工学报, 2025,46(9):240894.
Peng YIN, Fenglei HUANG, Keren SHI, et al. An Improved Particle Swarm Optimization Algorithm for Optimizing the Aiming Point of Multiple Projectiles against Surface Targets[J]. Acta Armamentarii, 2025, 46(9): 240894.
尹鹏, 黄风雷, 石科仁, 等. 基于改进粒子群优化算法的多弹打击面目标瞄准点优化方法[J]. 兵工学报, 2025,46(9):240894. DOI: 10.12382/bgxb.2024.0894.
Peng YIN, Fenglei HUANG, Keren SHI, et al. An Improved Particle Swarm Optimization Algorithm for Optimizing the Aiming Point of Multiple Projectiles against Surface Targets[J]. Acta Armamentarii, 2025, 46(9): 240894. DOI: 10.12382/bgxb.2024.0894.
针对多弹打击复杂形状面目标时的瞄准点优选问题
提出一种改进粒子群优化算法(Improved Particle Swarm Optimization
IPSO)优化多弹最佳瞄准点。构建瞄准点选择模型时
综合考虑面目标区域形状、区域关联、弹药威力区域、弹药命中精度、累积毁伤和多弹联合毁伤等复杂因素对目标毁伤效果的影响。通过预分配粒子位置和引入粒子活化能改进了粒子群优化算法
在提升算法收敛速度的同时又确保了其全局搜索能力
通过典型复杂面目标测试用例验证算法性能。研究结果表明
IPSO算法相比于蒙特卡洛算法、粒子群优化算法和改进灰狼优化算法具有更优的瞄准点选择能力
平均毁伤收益提升4.3%
且平均瞄准点选择耗时仅为其1/4~1/3
在毁伤收益和计算效率上具有明显优势。
An improved particle swarm optimization (IPSO) algorithm is proposed to optimize the optimal aiming point of multiple projectiles against complex-shaped surface targets.When constructing an aiming point selection model
the influences of complex factors such as surface target area shape
regional correlation
ammunition power area
ammunition hit accuracy
cumulative damage and multi-projectiles combined damage on target damage effect are considered comprehensively.Particle swarm optimization (PSO) algorithm is improved by preassigning the particle positions and introducing the particle activation energy
which not only improves the convergence speed of the algorithm but also ensures the global search ability.The proposed algorithm is verified through typical complex target test cases.The results show that
compared with Monte Carlo algorithm
PSO algorithm and improved grey wolf optimization algorithm
the IPSO algorithm has a better ability to select aiming points
and increases the average damage yield by 4.3%.And the average computation time for aiming point selection is only 1/4-1/3 of that of traditional optimization algorithms.
which has obvious advantages in damage income and computing efficiency.
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高天运 , 马兰 , 齐建成 . 外军高超声速武器作战及其目标杀伤链构建分析 [J ] . 战术导弹技术 , 2024 , 45 ( 3 ): 136 - 147 .
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MOON S H . Weapon effectiveness and the shapes of damage functions [J ] . Defence Technology , 2021 , 17 ( 2 ): 617 - 632 . DOI: 10.1016/j.dt.2020.04.009 http://doi.org/10.1016/j.dt.2020.04.009 This paper provides a review of methods of assessing a fragmentation weapon's effectiveness against a point target or an area target with keeping the focus on the necessity of using the Carleton damage function with the correct shape factor. First, cookie-cutter damage functions are redefined to preserve the shape factor of and to have the same lethal area as the corresponding Carleton damage function. Then, closed-form solutions of the effectiveness methods are obtained by using those cookie-cutter damage functions and the Carleton damage function. Finally, the closed-form solutions are applied to calculate the probability of damaging a point target and the expected fractional damage to an area target for several attack scenarios by using cookie-cutter damage functions and the Carleton damage functions with different shape factors. The comparison of the calculation results shows that using cookie-cutter damage functions or the Carleton damage function with a wrong shape factor results in quite significant differences from using the original Carleton damage function with a correct shape factor when weapon's delivery error deviations are less than or comparable to the lengths of the lethal area and the aim point is far from a target. The effectiveness methods improved in this paper will be useful for mission planning utilizing the precision-guided munitions in circumstances where the collateral damage should be reduced. © 2020 The Author
卢发兴 , 贾正荣 , 王航宇 , 等 . 对任意分布目标的舰炮对面区域射击瞄准点配置方法 [J ] . 系统工程与电子技术 , 2019 , 41 ( 6 ): 1278 - 1285 . DOI: 10.3969/j.issn.1001-506X.2019.06.15 http://doi.org/10.3969/j.issn.1001-506X.2019.06.15 为解决目标服从不规则分布情况下,舰炮对面射击瞄准点配置求解困难的问题,提出对任意分布目标的舰炮对面区域射击瞄准点配置方法。通过在离散空间内描述目标分布概率密度函数,直接给出数值形式的目标分布概率密度函数,避免解析形式下多次更新、更改对目标概率密度函数复杂度的提升;基于变分分析,求解数值形式的最优有效范围与最优中间函数;基于最优有效范围进行射击瞄准点初分配,之后基于共轭梯度法与最优中间函数进行射击瞄准点配置优化。仿真分析中,在目标服从规则分布情况下分别采用现有方法与所提方法求解瞄准点配置与对应毁伤概率,在目标服从不规则分布情况下采用所提方法求解瞄准点配置,并通过统计模拟法对比验证毁伤概率。结果表明,该方法在目标服从规则分布情况下与现有方法优化程度相当;同时,该方法能够求解目标服从不规则分布情况下的瞄准点配置,对应毁伤概率与理论最优毁伤概率相近。
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