兵工学报2024,Vol.45Issue(z2) :47-54.DOI:10.12382/bgxb.2024.0859

基于KAN的舰载高功率微波武器目标威胁评估

KAN-based Target Threat Assessment of Shipboard High-power Microwave Weapon

徐洪浩 曹巍 章业超 陈志华
兵工学报2024,Vol.45Issue(z2) :47-54.DOI:10.12382/bgxb.2024.0859

基于KAN的舰载高功率微波武器目标威胁评估

KAN-based Target Threat Assessment of Shipboard High-power Microwave Weapon

徐洪浩 1曹巍 2章业超 1陈志华1
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作者信息

  • 1. 南京理工大学 瞬态物理国家重点实验室,江苏 南京 210094
  • 2. 陆军装备部驻重庆地区军事代表局,重庆 400000
  • 折叠

摘要

随着无人机等现代化电子设备在战场上的广泛应用,高功率微波(High Power Microwave,HPM)武器在未来海上防空作战中展现出巨大的潜力.HPM武器与常规舰载防空武器协同防空时的威胁评估成为亟待解决的核心问题,建立基于KAN(Kolmogorov-Arnold Network)的威胁评估模型,通过量化6 项核心因素对来袭目标的威胁程度进行评估.仿真实验表明:KAN模型的威胁评估误差保持在0.036 以下,平均误差为0.0142,优于传统的BP(Back Propagation)网络模型和FABP(Firefly Algorithm Optimized Back Propagation)网络模型.加入HPM武器后,对相应量化指标进行调整,运用KAN模型预测加入HPM武器后的目标威胁值,目标威胁值平均下降了0.0714.HPM武器的使用显著增强了舰艇的防空能力,为未来防空系统的设计提供了重要指导.

Abstract

With the widespread application of modern electronic equipment,such as drones,on the battlefield,the high-power microwave(HPM)weapons have shown great potential in future maritime air defense operations.A key issue that needs to be addressed is the threat assessment when HPM weapons are used in conjunction with conventional shipborne air defense weapons.To tackle this,a threat assessment model based on the Kolmogorov-Arnold network(KAN)is established for evaluating the threat level of incoming targets by quantifying six core factors.Simulation experiments show that the KAN model maintains a threat assessment error below 0.036 with an average error of 0.014 2,outperforming traditional BP network models and FABP network models.After incorporating HPM weapons,the corresponding quantitative indicators are adjusted,and the KAN model is used to predict the target threat values.The average target threat value is decreased by 0.071 4.The HPM weapons can be usedsignificantly to enhance the air defense capabilities of naval vessels.

关键词

高功率微波武器/威胁评估/Kolmogorov-Arnold网络/残差激活函数/稀疏化训练

Key words

high-power microwave weapon/target threat assessment/Kolmogorov-Arnold network/residual activation function/sparse training

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出版年

2024
兵工学报
中国兵工学会

兵工学报

CSTPCD北大核心
影响因子:0.735
ISSN:1000-1093
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