Image-guided missile terminal artificial capture and control virtual training system

By constructing a three-dimensional battlefield geometric model and a neurodynamic model, and combining eye tracking and physiological data, the problems of environmental distortion and single evaluation dimension in the virtual training system are solved, and a realistic simulation and detailed evaluation of the terminal acquisition and control process of image-guided missiles are realized.

CN122408541APending Publication Date: 2026-07-17BEIJING SHIJICHEN DATA TECH CO LTD
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
BEIJING SHIJICHEN DATA TECH CO LTD
Filing Date
2026-04-27
Publication Date
2026-07-17

AI Technical Summary

Technical Problem

Existing virtual training systems cannot realistically reflect image degradation under complex electromagnetic environments during the terminal phase acquisition and control of simulated image-guided missiles. Furthermore, they have a single evaluation dimension and cannot assess the physiological stress and operational logic of operators under extreme conditions.

Method used

A three-dimensional battlefield geometry model is constructed, and a neurodynamic model is used to simulate nonlinear image degradation under complex electromagnetic and physical environments. Combined with eye tracking and physiological data, a detailed training report is generated through virtual-real mapping and attribution evaluation modules.

Benefits of technology

It achieves realistic simulation of complex electromagnetic environments, can assess the operator's physiological state and control logic, and provides detailed training reports, thus improving the realism of training and the accuracy of assessment.

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Abstract

本发明公开了图像制导导弹末段人工捕控虚拟训练系统,涉及导弹模拟训练技术领域,具体包括:环境仿真模块、交互采集模块、虚实映射模块以及归因评估模块;构建三维战场几何模型,利用神经动力学模型模拟复杂电磁与物理环境下的非线性图像退化效应;采集操控指令、眼动追踪和生理数据,并进行时间对齐、特征提取和空间映射,根据生理数据识别操作员应激状态以自适应调整系统响应和区分认知偏差;以仿真时间为基准,对齐并插值导弹运动参数和多模态数据,检测并处理延迟超限,形成原始捕控特征向量并增强捕控特征向量,并进行有效性检验后输出;通过支持向量数据描述学习超球体边界,进行分类、量化偏差强度,生成归因报告和综合训练报告。
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