基于块正交匹配追踪预处理的米波多输入多输出雷达测高方法研究

郑桂妹 宋玉伟 胡国平 李槟槟 张栋

郑桂妹, 宋玉伟, 胡国平, 等. 基于块正交匹配追踪预处理的米波多输入多输出雷达测高方法研究[J]. 雷达学报, 2020, 9(5): 908–915. doi: 10.12000/JR20042
引用本文: 郑桂妹, 宋玉伟, 胡国平, 等. 基于块正交匹配追踪预处理的米波多输入多输出雷达测高方法研究[J]. 雷达学报, 2020, 9(5): 908–915. doi: 10.12000/JR20042
ZHENG Guimei, SONG Yuwei, HU Guoping, et al. Height measurement for meter-wave MIMO radar based on block orthogonal matching pursuit preprocessing[J]. Journal of Radars, 2020, 9(5): 908–915. doi: 10.12000/JR20042
Citation: ZHENG Guimei, SONG Yuwei, HU Guoping, et al. Height measurement for meter-wave MIMO radar based on block orthogonal matching pursuit preprocessing[J]. Journal of Radars, 2020, 9(5): 908–915. doi: 10.12000/JR20042

基于块正交匹配追踪预处理的米波多输入多输出雷达测高方法研究

DOI: 10.12000/JR20042
基金项目: 陕西省青年托举人才项目(20180109),国家自然科学基金面上项目(61871395, 61971438),陕西省自然科学基金面上项目(2019JM-155, 2020JM-345)
详细信息
    作者简介:

    郑桂妹(1987–),男,福建福州人,副教授,研究方向为阵列空间谱估计算法

    宋玉伟(1986–),女,辽宁大连人,博士研究生,研究方向为MIMO雷达谱估计

    胡国平(1964–),男,江西南昌人,教授,研究方向为阵列信号处理

    李槟槟(1990–),男,江西上饶人,讲师,研究方向为阵列空间谱估计

    张 栋(1990–),男,河北辛集人,工程师,研究方向为阵列空间谱估计

    通讯作者:

    郑桂妹 zheng-gm@163.com

  • 责任主编:陈伯孝 Corresponding Editor: CHEN Baixiao
  • 中图分类号: TN953

Height Measurement for Meter-wave MIMO Radar Based on Block Orthogonal Matching Pursuit Preprocessing

Funds: The Young Talent fund of University Association for Science and Technology in Shaanxi of China(20180109), The National Natural Science Foundation of China(61871395, 61971438), The Natural Science Basic Research Plan in Shaanxi Province of China (2019JM-155, 2020JM-345)
More Information
    Corresponding author: ZHENG Guimei, zheng-gm@163.com
  • 摘要: 米波雷达具有很好的反隐身性能。多输入多输出(MIMO)雷达的波形分集具有高自由度特点,使MIMO雷达在检测和参数估计等方面具有更多优势,故米波MIMO雷达受到广泛研究。而测高是米波MIMO雷达最重要的问题之一。针对米波MIMO雷达测高问题,最大似然和广义多重信号分类方法是米波MIMO阵列雷达测高方法行之有效的算法,但其计算量大,工程中难以接受。该文提出一种基于块正交匹配追踪(BOMP)预处理的方法来降低计算量。首先对MIMO阵列接收数据稀疏化处理,然后通过数学操作将其变形至适合于BOMP算法的信号模型,然后利用粗栅格得到角度粗估计。并以此为初始值中心,取MIMO雷达波束宽度作为搜索范围。仿真结果表明该算法能有效降低搜索类测高算法的计算量。

     

  • 图  1  米波MIMO雷达低仰角测高镜面反射模型示意图

    Figure  1.  Schematic diagram of height measurement of low elevation target with meter wave MIMO radar under specular reflection model

    图  2  基于BOMP稀疏恢复的支撑位置估计结果

    Figure  2.  Support position estimation results based on BOMP sparse recovery algorithm

    图  3  最大似然和广义MUSIC的全空域搜索估计结果

    Figure  3.  Estimation results of maximum likelihood and generalized MUSIC under whole airspace searching

    图  4  10次独立实验的基于BOMP预处理后最大似然的搜索范围结果

    Figure  4.  10 independent experiments results based on maximum likelihood after BOMP preprocessing

    图  5  10次独立实验的基于BOMP预处理后广义MUSIC的搜索范围结果

    Figure  5.  10 independent experiments results based on generalized MUSIC after BOMP preprocessing

    图  6  最大似然和广义MUSIC算法的角度估计RMSE

    Figure  6.  RMSE of angle estimation with maximum likelihood and generalized MUSIC algorithms

    图  7  最大似然和广义MUSIC算法的高度测量RMSE

    Figure  7.  RMSE of height measurement with maximum likelihood and generalized MUSIC algorithms

    图  8  预处理和非预处理算法运算时间比较结果

    Figure  8.  Comparison results of run time of preprocessing and non-preprocessing algorithms

    表  1  阵列雷达噪声子空间和导向矢量、导向矩阵的正交性

    Table  1.   Orthogonality of noise subspace of array radar and steering vector, manifold matrix

    噪声子空间导向矢量导向矩阵(信号子空间)
    常规阵列雷达正交(MUSIC)正交(广义MUSIC)
    MIMO阵列雷达不正交(MUSIC)正交(广义MUSIC)
    下载: 导出CSV

    表  2  BOMP算法的计算流程

    Table  2.   Calculation process of BOMP algorithm

     输入:匹配滤波后的矢量化数据、角度网格数、目标数。
     初始化:用接收数据初始化残差、用角度网格数初始化字典、初
    始化支撑集。
     迭代:(1) 利用残差和字典计算投影;
        (2) 根据投影寻找块最大的坐标值,并将此值坐标放入块
    支撑集;
        (3) 利用块支撑集更新残差;
        (4) 迭代(1)至(3),迭代次数达到目标数停止。
     输出:利用块支撑集计算块支撑向量。
    下载: 导出CSV
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出版历程
  • 收稿日期:  2020-04-17
  • 修回日期:  2020-06-04
  • 网络出版日期:  2020-10-28

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