基于新特征参数的再入滑翔飞行器机动模式智能辨识  

Intelligent Recognition of Maneuver Modes for Reentry Gliding Vehicle Based on New Featuse Parameters

作　　者：贺杨超 李炯[1] 邵雷[1] 周池军[1] 张锦林 HE Yangchao;LI Jiong;SHAO Lei;ZHOU Chijun;ZHANG Jinlin(Air Defense and Antimissle School,Air Force Engineering University,Xi’an 710038,China)

机构地区：[1]空军工程大学防空反导学院,西安710038

出　　处：《空军工程大学学报》2024年第2期32-38,共7页Journal of Air Force Engineering University

基　　金：国家自然科学基金(62173339)。

摘　　要：再入滑翔飞行器的机动模式辨识问题是拦截方实现对其轨迹预测的关键。提出了一组与飞行器轨迹机动特点贴合的特征参数,根据构建的RGV机动模式轨迹库,搭建了LSTM深度学习神经网络,实现了对RGV机动模式的智能辨识。与传统模式辨识方法和其他典型特征参数的辨识网络进行对比,结果显示文中所提特征参数在LSTM机动模式辨识网络训练中具有收敛速度快、辨识精度高和鲁棒性好的特点。The maneuver mode recognition problem of reentry gliding vehicle(RGV)is the key to the interceptors in achieving its trajectory prediction.In view of this issue,this paper proposes a set of feature parameters fitted to the maneuver characteristics of vehicle trajectories.Based on the constructed RGV maneuvering mode trajectory library,an LSTM deep learning neural network is built,training the extracted new feature parameters.Compared with the traditional modes recognition method and other typical feature parameters in network training,the results show that the set of the proposed feature parameters is fast at convergence speed,high in recognition accuracy,and good in robustness in LSTM maneuver mode recognition network training.

关 键 词：再入滑翔飞行器 特征参数 机动模式 智能辨识 

分 类 号：V448.235[航空宇航科学与技术—飞行器设计]