基于SVM和EKF的高超声速滑翔飞行器轨迹预报

doi:10.13700/j.bh.1001-5965.2020.0050

北京航空航天大学学报 ›› 2020, Vol. 46 ›› Issue (11): 2094-2105.doi: 10.13700/j.bh.1001-5965.2020.0050

基于SVM和EKF的高超声速滑翔飞行器轨迹预报

程云鹏¹, 孙成志¹, 闫晓东^1,2

1. 西北工业大学航天学院, 西安 710072;
2. 陕西省空天飞行器设计技术重点实验室, 西安 710072

收稿日期:2020-02-25 发布日期:2020-12-01
通讯作者: 闫晓东 E-mail:yan804@nwpu.edu.cn
作者简介:程云鹏,男,博士研究生。主要研究方向:机动目标跟踪和预报;孙成志,男,硕士研究生。主要研究方向:航天器故障诊断和识别;闫晓东,男,博士,副教授,硕士生导师。主要研究方向:飞行器动力学与制导。

Trajectory prediction of hypersonic glide vehicle based on SVM and EKF

CHENG Yunpeng¹, SUN Chengzhi¹, YAN Xiaodong^1,2

1. School of Astronautics, Northwestern Polytechnical University, Xi'an 710072, China;
2. Shaanxi Aerospace Flight Vehicle Design Key Laboratory, Xi'an 710072, China

Received:2020-02-25 Published:2020-12-01

摘要/Abstract

摘要： 高超声速滑翔飞行器（HGV）拦截问题中，轨迹预报是成功拦截的重要基础。针对HGV机动能力强、轨迹多变的特点，提出了一种基于支持向量机（SVM）和扩展卡尔曼滤波（EKF）的轨迹预报方法。在HGV的滑翔段机动模式分析的基础上，将HGV的机动运动分解为纵向运动模式和侧向运动模式，进而对运动模式的特征参数予以标定，形成SVM的训练集。建立地基单雷达轨迹跟踪模型，采用EKF对HGV滑翔段轨迹进行稳定跟踪并实现对运动模式特征参数的估计。基于SVM，建立了HGV运动识别框架，实现了对HGV滑翔段轨迹的预报。对平衡滑翔和跳跃机动2种典型机动模式进行数学仿真验证，结果表明，所提方法可以提高对该类目标的轨迹预报精度。

关键词: 高超声速滑翔飞行器(HGV), 机动模式, 支持向量机(SVM), 运动识别, 轨迹估计和预报

Abstract: In the scenario of intercepting a Hypersonic Glide Vehicle (HGV), the trajectory prediction is a key issue for successful interception. Considering HGV's strong maneuverability and variable trajectory, in this paper, a novel trajectory prediction method is proposed based on Support Vector Machine (SVM) and Extended Kalman Filter (EKF). First, the investigation on the maneuvering mode is performed. The maneuver motion of the HGV is divided into longitudinal mode and lateral mode, which are labeled and formulated into the training set of SVMs. Second, the tracking model of the trajectory for single ground-based radar is established, and EKF is applied to track the glide trajectory of HGV. Finally, the recognition framework of HGV motion is established based on SVM, and the prediction of the subsequent trajectory is accomplished. The results show that the proposed method can improve the trajectory prediction accuracy of HGV.

Key words: Hypersonic Glide Vehicle (HGV), maneuver mode, Support Vector Machine (SVM), motion recognition, trajectory estimation and prediction

中图分类号:

V19
TN953

程云鹏, 孙成志, 闫晓东. 基于SVM和EKF的高超声速滑翔飞行器轨迹预报[J]. 北京航空航天大学学报, 2020, 46(11): 2094-2105.

CHENG Yunpeng, SUN Chengzhi, YAN Xiaodong. Trajectory prediction of hypersonic glide vehicle based on SVM and EKF[J]. Journal of Beijing University of Aeronautics and Astronautics, 2020, 46(11): 2094-2105.

参考文献

[1] 张灿,林旭斌,刘都群,等.2019年国外高超声速飞行器技术发展综述[J].飞航导弹,2020(1):16-20.ZHANG C,LIN X B,LIU D Q,et al.Review of the development of hypersonic vehicle technology in 2019[J].Aerodynamic Missile Journal,2020(1):16-20(in Chinese).
[2] 林旭斌,李彦,胡冬冬.美军2019财年高超声速科研预算简析[J].飞航导弹,2018(6):1-3.LIN X B,LI Y,HU D D.Analysis on the budget of hypersonic scientific research of the US military in 2019[J].Aerodynamic Missile Journal,2018(6):1-3(in Chinese).
[3] LI G H,ZHANG H B,TANG G J.Maneuver characteristics analysis for hypersonic glide vehicles[J].Aerospace Science and Technology,2015,43:321-328.
[4] ZARCHAN P.Tactical and strategic missile guidance[M].Reston:AIAA,2012.
[5] QIN L,LI J L,ZHOU D.Tracking filter and prediction for non-ballistic target HTV-2 in near space[C]//27th Chinese Control and Decision Conference.Piscataway:IEEE Press,2015:3556-3561.
[6] KIM K,HWANG I.Intent-based detection and characterization of aircraft maneuvers in en route airspace[J].Journal of Aerospace Information Systems,2018,15(2):72-91.
[7] 张洪波,黄景帅,李广华,等.典型控制规律滑翔飞行器的轨迹预测方法[J].现代防御技术,2017,45(4):112-118.ZHANG H B,HUANG J S,LI G H,et al.Trajectory prediction of glide vehicle based on typical control law[J].Modern Defence Technology,2017,45(4):112-118(in Chinese).
[8] VAPNIK N.The nature of statistical learning theory[M].Berlin:Springer,1995.
[9] BERNHARD S.Advances in kernel methods:Support vector learning[M].Cambridge:MIT Press,1999.
[10] 于震梁,孙志礼,曹汝男,等.基于支持向量机和卡尔曼滤波的机械零件剩余寿命预测模型研究[J].兵工学报,2018,39(5):991-997.YU Z L,SUN Z L,CAO R N,et al.Research on remaining useful life predictive model of machine parts based on SVM and Kalman filter[J].Acta Armamentarii,2018,39(5):991-997(in Chinese).
[11] 陈旭梅,龚辉波,王景楠.基于SVM和Kalman滤波的BRT行程时间预测模型研究[J].交通运输系统工程与信息,2012,12(4):29-34.CHEN X M,GONG H B,WANG J N.BRT vehicle travel time prediction based on SVM and Kalman filter[J].Journal of Transportation Systems Engineering and Information Technology,2012,12(4):29-34(in Chinese).
[12] 杨茂,黄宾阳,江博,等.基于卡尔曼滤波和支持向量机的风电功率实时预测研究[J].东北电力大学学报,2017,37(2):45-51.YANG M,HUANG B Y,JIANG B,et al.Real-time prediction for wind power based on Kalman filter and support vector machines[J].Journal of Northeast Electric Power University,2017,37(2):45-51(in Chinese).
[13] PENG H,BAI X L.Exploring capability of support vector machine for improving satellite orbit prediction accuracy[J].Journal of Aerospace Information Systems,2018,15(6):366-381.
[14] VINH N X.Optimal trajectories in atmospheric flight[M].London:Pergamon Press Ltd.,1982:449-468.
[15] 赵汉元.飞行器再入动力学与制导[M].长沙:国防科技大学出版社,1997.ZHAO H Y.Reentry dynamics and guidance of aircraft[M].Changsha:University of National Defense Science and Technology Press,1997(in Chinese).
[16] LI G H,ZHANG H B,TANG G J,et al.Maneuver modes analysis for hypersonic glide vehicles[C]//Proceedings of 2014 IEEE Chinese Guidance,Navigation and Control Conference.Piscataway:IEEE Press,2014:543-548.
[17] ZHU J W,LIU L H,TANG G J,et al.Highly constrained optimal gliding guidance[J].Proceedings of the Institution of Mechanical Engineers,Part G:Journal of Aerospace Engineering,2015,229(12):2321-2335.
[18] 李凡,熊家军,张凯,等.临近空间高超声速目标跟踪动力学模型[J].宇航学报,2019,40(3):266-276.LI F,XIONG J J,ZHANG K,et al.Near space hypersonic target dynamics tracking model[J].Journal of Astronautics,2019,40(3):266-276(in Chinese).
[19] 谢愈,刘鲁华,汤国建,等.高超声速滑翔飞行器摆动式机动突防弹道设计[J].航空学报,2011,32(12):2174-2181.XIE Y,LIU L H,TANG G J,et al.Weaving maneuver trajectory design for hypersonic glide vehicle[J].Acta Aeronautica et Astronautica Sinica,2011,32(12):2174-2181(in Chinese).
[20] 王丹.基于运动行为识别的高超声速目标弹道估计[D].哈尔滨:哈尔滨工业大学,2017.WANG D.Trajectory estimation of hypersonic target based on motion behavior recognition[D].Harbin:Harbin Institute of Technology,2017(in Chinese).
[21] HOUGH M E.Reentry maneuver estimation using nonlinear Markov acceleration models[J].Journal of Guidance,Control,and Dynamics,2017,40(7):1693-1710.
[22] 张凯,熊家军,韩春耀,等.一种基于气动力模型的高超声速滑翔目标跟踪算法[J].宇航学报,2017,38(2):123-130.ZHANG K,XIONG J J,HAN C Y,et al.A tracking algorithm of hypersonic glide reentry vehicle via aerodynamic model[J].Journal of Astronautics,2017,38(2):123-130(in Chinese).
[23] SINGER R A.Estimating optimal tracking filter performance for manned maneuvering targets[J].IEEE Transactions on Aerospace and Electronic Systems,1970,AES-6(4):473-483.
[24] ZHU J W,HE R Z,TANG G J,et al.Pendulum maneuvering strategy for hypersonic glide vehicles[J].Aerospace Science and Technology,2018,78:62-70.
[25] HOU Q L,ZHANG J X,LIU L M,et al.Discriminative information-based nonparallel support vector machine[J].Signal Processing,2019,162:169-179.
[26] CHANG M,LIN C.Leave-one-out bounds for support vector regression model selection[J].Neural Computation,2005,17(5):1188-1222.
[27] CHANG C,LIN C.LIBSVM:A library for support vector machines[J].ACM Transactions on Intelligent Systems and Technology,2011,2(3):1-27.
[28] KJELDSEN H T.A contextualized historical analysis of the Kuhn-Tucker theorem in nonlinear programming:The impact of World War Ⅱ[J].Historia Mathematica,2000,27(4):331-361.
[29] CHEN K L,YU J.Short-term wind speed prediction using an unscented Kalman filter based state-space support vector regression approach[J].Applied Energy,2014,113:690-705.
[30] 张强,肖刚,蓝屹群.基于BP神经网络多类分类的湍流目标探测[J].系统工程与电子技术,2018,40(7):1486-1490.ZHANG Q,XIAO G,LAN Y Q.Turbulence target detection based on BP neural network multi-level classification[J].Systems Engineering and Elctronics,2018,40(7):1486-1490(in Chinese).
[31] 何伟.基于朴素贝叶斯的文本分类算法研究[D].南京:南京邮电大学,2018.HE W.Text classification algorithm research based on naive Bayes[D].Nanjing:Nanjing University of Posts and Telecommunications,2018(in Chinese).

基于SVM和EKF的高超声速滑翔飞行器轨迹预报

Trajectory prediction of hypersonic glide vehicle based on SVM and EKF

RichHTML

PDF (PC)

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 10

编辑推荐

Metrics

本文评价

[1]	孙波, 梁勇, 汉牟田, 杨磊, 荆丽丽, 俞永庆. 基于GA-SVM的GNSS-IR土壤湿度反演方法[J]. 北京航空航天大学学报, 2019, 45(3): 486-492.
[2]	曹惠玲, 高升, 薛鹏. 基于多分类AdaBoost的航空发动机故障诊断[J]. 北京航空航天大学学报, 2018, 44(9): 1818-1825.
[3]	陈唯实, 闫军, 张洁, 李敬. 基于支持向量机的机场智能驱鸟决策[J]. 北京航空航天大学学报, 2018, 44(7): 1547-1553.
[4]	韩冬, 张学军, 聂尊礼, 管祥民. 一种基于SVM的低空飞行冲突探测算法[J]. 北京航空航天大学学报, 2018, 44(3): 576-582.
[5]	黄洁, 姜志国, 张浩鹏, 姚远. 基于卷积神经网络的遥感图像舰船目标检测[J]. 北京航空航天大学学报, 2017, 43(9): 1841-1848.
[6]	张建, 李艳军, 曹愈远, 张丽娜. 免疫支持向量机用于航空发动机磨损故障诊断[J]. 北京航空航天大学学报, 2017, 43(7): 1419-1425.
[7]	葛宇龙, 李晓星, 郎利辉, 程鹏志. 基于机器学习的管材数控弯曲质量预测[J]. 北京航空航天大学学报, 2016, 42(8): 1691-1697.
[8]	李海涛, 何玉珠, 宋平. 基于量子万有引力搜索的SVM自驾故障诊断[J]. 北京航空航天大学学报, 2016, 42(6): 1093-1098.
[9]	陈彬, 韩超, 刘阁. 铜颗粒污染物对变压器油运动黏度的影响[J]. 北京航空航天大学学报, 2016, 42(10): 2031-2037.
[10]	李可, 刘祎, 杜少毅, 孙毅, 王浚. 基于PCA和WPSVM的航天器电特性识别方法[J]. 北京航空航天大学学报, 2015, 41(7): 1177-1182.