基于深度学习的多高超声速飞行器时间协调进入制导方法

Z. Li, J. Guo, S. Tang, S. Ji
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引用次数: 1

摘要

针对高超声速滑翔飞行器进入阶段的协同制导问题,提出了一种基于深度学习的多飞行器时间协调制导技术。为了满足时间协调的要求,纵向导向采用双参数倾斜角轮廓。利用深度神经网络(DNN)结构构建车辆轨迹数据库,并利用训练好的网络代替传统的预测方法。构造扩展卡尔曼滤波器实时检测气动参数的变化,并将气动参数输入深度神经网络。横向制导采用了一种基于分段航向角误差廊道的逆岸角符号逻辑。最后的仿真结果表明,所构建的深度神经网络能够满足协同引导的要求。该算法具有制导精度高、响应时间快、不需要弹间通信等优点,即使在气动参数发生干扰的情况下也能求解出满足飞行要求的制导指令。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A deep learning-based approach to time-coordination entry guidance for multiple hypersonic vehicles
A multiple-vehicles time-coordination guidance technique based on deep learning is suggested to address the cooperative guiding problem of hypersonic gliding vehicle entry phase. A dual-parameter bank angle profile is used in longitudinal guiding to meet the requirements of time coordination. A vehicle trajectory database is constructed along with a deep neural network (DNN) structure devised to fulfill the error criteria, and a trained network is used to replace the conventional prediction approach. Moreover, an extended Kalman filter is constructed to detect changes in aerodynamic parameters in real time, and the aerodynamic parameters are fed into a DNN. The lateral guiding employs a logic for reversing the sign of bank angle, which is based on the segmented heading angle error corridor. The final simulation results demonstrate that the built DNN is capable of addressing the cooperative guiding requirements. The algorithm is highly accurate in terms of guiding, has a fast response time, and does not need inter-munition communication, and it is capable of solving guidance orders that satisfy flight requirements even when aerodynamic parameter disruptions occur.
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