Online Generation Method for Interceptor Missile Launch Envelope Considering Complex Environmental Disturbances

WANG Xiaofang; ZHAO Yangyan

doi:10.12382/bgxb.2024.1096

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Online Generation Method for Interceptor Missile Launch Envelope Considering Complex Environmental Disturbances

更新时间：2026-04-24

- Online Generation Method for Interceptor Missile Launch Envelope Considering Complex Environmental Disturbances
- Acta Armamentarii Vol. 47, Issue 2, Pages: 241096(2026)
- 作者机构：
  
  北京理工大学宇航学院，北京 100081
- 作者简介：
- 基金信息：
- DOI：10.12382/bgxb.2024.1096
  CLC： V249.31
- Received：05 December 2024，
  
  Online First：25 December 2025，
  
  Published：28 February 2026
- 稿件说明：
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WANG Xiaofang, ZHAO Yangyan. Online Generation Method for Interceptor Missile Launch Envelope Considering Complex Environmental Disturbances[J]. Acta Armamentarii, 2026, 47(2): 241096.
DOI：

WANG Xiaofang, ZHAO Yangyan. Online Generation Method for Interceptor Missile Launch Envelope Considering Complex Environmental Disturbances[J]. Acta Armamentarii, 2026, 47(2): 241096. DOI： 10.12382/bgxb.2024.1096.

摘要

针对复杂环境干扰下拦截弹拦截高超滑翔目标的攻击区在线求解问题，提出一种以径向基函数（Radial Basis Function，RBF）神经网络为基模型、通过自注意力（Self-Attention，SA）机制集成的RBF+SA攻击区网络模型。建立考虑风和大气密度扰动等环境干扰的拦截弹运动方程组，在给定目标运动、拦截弹机动能力和制导策略的基础上，定义以拦截弹初始位置和初始速度方向表征的攻击区，并离线获得环境干扰下的攻击区样本数据。考虑不同方向风、大气密度等多项环境干扰对拦截弹攻击区影响的强非线性，基于RBF神经网络建立风、大气密度等单干扰下的攻击区网络基模型，再建立SA机制实现不同作战场景中对单干扰攻击区基模型的动态集成，形成具有较快训练速度和良好精度的多环境干扰下攻击区网络模型。仿真结果表明，攻击区RBF+SA网络模型能够以较高的精度表征复杂环境干扰下的攻击区，且能以较少的样本数据实现更高的攻击区预测精度。

Abstract

A radial basis function(RBF)+self-attention(SA)launch envelope network model

which uses RBF neural network as the base model and is integrated with SA mechanism

is proposed to address the online calculation problem of the launch envelope when an interceptor missile intercepts a hypersonic gliding target under complex environmental disturbances. The equations of motion for the interceptor missile considering environmental disturbances such as wind and atmospheric density perturbations are established

and a launch envelope characterized by the initial positions and initial velocity direction of interceptor missile is defined based on the given target motion

interceptor missile maneuverability

and guidance strategy. Additionally

the sample data for the launch envelope are obtained offline. Then considering the strong nonlinearity of the impact of multiple environmental disturbances on the interceptor missile's launch envelope

the network-based models for the launch envelope under single disturbances(e.g.

wind and atmospheric density)are established using RBF neural network

and a SA mechanism is developed to dynamically integrate the single-disturbance launch envelope base models across various combat scenarios

thus forming a launch envelope network model which has fast training speed and high accuracy under multiple environmental disturbances. The results demonstrate that the RBF+SA neural network model accurately represents the launch envelope under multiple complex environmental disturbances with high precision and achieves higher prediction accuracy using fewer samples.

关键词

Keywords

references

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