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基于耦合气动参数的HGV多模型估计

张凯 熊家军 付婷婷 席秋实

张凯, 熊家军, 付婷婷, 等 . 基于耦合气动参数的HGV多模型估计[J]. 北京航空航天大学学报, 2018, 44(10): 2156-2164. doi: 10.13700/j.bh.1001-5965.2018.0028
引用本文: 张凯, 熊家军, 付婷婷, 等 . 基于耦合气动参数的HGV多模型估计[J]. 北京航空航天大学学报, 2018, 44(10): 2156-2164. doi: 10.13700/j.bh.1001-5965.2018.0028
ZHANG Kai, XIONG Jiajun, FU Tingting, et al. Multi-model estimation of HGV based on coupled aerodynamic parameters[J]. Journal of Beijing University of Aeronautics and Astronautics, 2018, 44(10): 2156-2164. doi: 10.13700/j.bh.1001-5965.2018.0028(in Chinese)
Citation: ZHANG Kai, XIONG Jiajun, FU Tingting, et al. Multi-model estimation of HGV based on coupled aerodynamic parameters[J]. Journal of Beijing University of Aeronautics and Astronautics, 2018, 44(10): 2156-2164. doi: 10.13700/j.bh.1001-5965.2018.0028(in Chinese)

基于耦合气动参数的HGV多模型估计

doi: 10.13700/j.bh.1001-5965.2018.0028
基金项目: 

国家“863”计划 2015AA7326042

国家“863”计划 2015AA8321471

全军军事类研究生资助课题 2016JY312

详细信息
    作者简介:

    张凯  男, 博士研究生。主要研究方向:高超声速飞行器预警探测

    熊家军  男, 博士, 教授, 博士生导师。主要研究方向:预警情报分析、信息融合

    付婷婷  女, 硕士, 讲师。主要研究方向:军事信息系统

    席秋实  女, 博士, 工程师。主要研究方向:预警情报分析

    通讯作者:

    熊家军, E-mail:124611796@qq.com

  • 中图分类号: V557+.1;TN953;V412.1

Multi-model estimation of HGV based on coupled aerodynamic parameters

Funds: 

National High-tech Research and Development Program of China 2015AA7326042

National High-tech Research and Development Program of China 2015AA8321471

Foundation for Military Postgraduates of China 2016JY312

More Information
  • 摘要:

    利用气动参数对未知气动力建模是提高高超声速滑翔目标跟踪精度的有效途径。对目标气动加速度及其导数项进行分析,在非耦合气动参数模型的基础上,考虑气动加速度在转弯和俯仰方向存在的先验信息,推导滚转和螺旋2种耦合气动参数模型。利用一种分离估计模型对目标状态与气动参数进行估计,分别给出状态滤波器和气动参数滤波器的表达式。同时,考虑不同飞行模式下参数的机动频率,构建基于耦合气动参数的交互多模型跟踪算法。仿真表明,本文所提算法精度显著高于针对该类目标的其他跟踪算法。同时,滚转模型的性能优于螺旋模型,且计算复杂度更小。

     

  • 图 1  分离估计模型结构

    Figure 1.  Structure of decomposed estimation model

    图 2  气动参数IMM示意图

    Figure 2.  IMM schematic of aerodynamic parameter

    图 3  不同模型位置和速度估计RMSE

    Figure 3.  RMSE in position and velocity estimate of different models

    图 4  不同动力学模型气动参数估计值

    Figure 4.  Aerodynamic parameter estimation values of different dynamic models

    图 5  不同动力学模型气动参数估计RMSE

    Figure 5.  RMSE in aerodynamic parameter estimate of different dynamic models

    图 6  不同滤波算法位置和速度估计RMSE

    Figure 6.  RMSE in position and velocity estimate for different filtering algorithms

    表  1  性能对比算例参数设置

    Table  1.   Parameter settings of cases for performance comparison

    算例滤波器量测参数
    噪声方差采样周期
    1IMM-EKFRxT
    2IMM-UKFRxT
    3IMM-EKF0.5RxT
    4IMM-EKF2RxT
    5IMM-EKFRx0.5T
    6IMM-EKFRx2T
    下载: 导出CSV

    表  2  位置、速度和气动参数估计RMSE均值对比

    Table  2.   Comparison of average RMSEs in position, velocity and aerodynamic parameters estimate

    算例位置RMSE
    均值/m
    速度RMSE均值/
    (m·s-1)
    αvRMSE均值/
    10-6
    αtRMSE均值/
    10-6
    αcRMSE均值/
    10-6
    MaRVSpiralBankMaRVSpiralBankMaRVSpiralBankMaRVSpiralBankMaRVSpiralBank
    1107.8105.797.0023.9423.0019.347.0696.7701.86180.8577.9571.2219.8620.0617.50
    3173.2165.6158.532.8729.5425.8910.667.7892.50084.2281.2874.3020.5821.6418.11
    4192.0187.4171.337.1232.6526.809.1328.5852.04786.5183.7876.3721.8222.1818.35
    585.6680.6176.6020.0119.4717.964.2274.1061.34374.2173.2668.3018.5819.6417.20
    6167.6160.3142.633.5932.8926.4818.0417.4912.56100.797.7089.9723.2523.3219.41
    下载: 导出CSV
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出版历程
  • 收稿日期:  2018-01-12
  • 录用日期:  2018-05-11
  • 刊出日期:  2018-10-20

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