| 一种高超声速飞行器在线反馈滤波算法 |
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| 引用本文： | 高长生,王越欣,荆武兴,胡玉东.一种高超声速飞行器在线反馈滤波算法[J].现代防御技术,2021(1). |
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| 作者姓名： | 高长生 王越欣 荆武兴 胡玉东 |
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| 作者单位： | 哈尔滨工业大学航天学院自主空间系统实验室 |
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| 摘 要： | 高超声速飞行器是近年来各国大力发展的新概念飞行器,针对其飞行运动轨迹难以进行高精度跟踪这一问题,结合神经网络强大的自适应和自学习能力,提出了一种高超声速飞行器在线反馈滤波算法。其核心是在当前统计模型的基础上,利用BP神经网络与卡尔曼滤波相结合进行滤波器设计,实现对高超声速飞行器高精度跟踪。最后通过仿真试验进行比较,验证了此在线反馈滤波算法在跟踪高超声速飞行器时的有效性。
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| 关 键 词： | 高超声速飞行器 轨迹跟踪 当前统计模型 卡尔曼滤波 BP神经网络 |
An Online Feedback Filtering Algorithm for Hypersonic Vehicle |
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| GAO Chang-sheng,WANG Yue-xin,JING Wu-xing,HU Yu-dong.An Online Feedback Filtering Algorithm for Hypersonic Vehicle[J].Modern Defence Technology,2021(1). |
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| Authors: | GAO Chang-sheng WANG Yue-xin JING Wu-xing HU Yu-dong |
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| Institution: | (Harbin Institute of Technology,School of Astronautics,Autonomous Space System Laboratory,Heilongjiang Harbin 150001,China) |
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| Abstract: | Hypersonic vehicle is a new concept vehicle developed by many countries in recent years.Aiming at the problem that it is difficult to track its flight trajectory with high precision,combined with the strong adaptive and self-learning ability of neural network,an online feedback filtering algorithm for hypersonic vehicle is proposed.Based on the current statistical model,BP neural network and Kalman filter are used to design the filter to realize the high-precision tracking of hypersonic vehicle.Finally,the simulation results show that the online feedback filtering algorithm is effective in tracking hypersonic vehicle. |
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| Keywords: | hypersonic vehicle trajectory tracking current statistical model Kalman filter BP neural network |
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