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基于稀疏和低秩恢复的稳健DOA估计方法

王洪雁 于若男

引用本文: 王洪雁, 于若男. 基于稀疏和低秩恢复的稳健DOA估计方法[J]. 电子与信息学报, doi: 10.11999/JEIT190263 shu
Citation:  Hongyan WANG, Ruonan YU. Sparse and Low Rank Recovery Based Robust DOA Estimation Method[J]. Journal of Electronics and Information Technology, doi: 10.11999/JEIT190263 shu

基于稀疏和低秩恢复的稳健DOA估计方法

    作者简介: 王洪雁: 男,1979年生,副教授,博士,研究方向为MIMO雷达信号处理、毫米波通信、机器视觉;
    于若男: 女,1995年生,硕士生,研究方向为阵列信号处理、毫米波通信
    通讯作者: 王洪雁,gglongs@163.com
  • 基金项目: 国家自然科学基金(61301258, 61271379),中国博士后科学基金(2016M590218),重点实验室基金(61424010106)

摘要: 该文针对有限次采样导致传统波达方向角(DOA)估计算法存在较大估计误差的问题,提出一种基于低秩恢复的稳健DOA估计方法。首先,基于低秩矩阵分解方法,将接收信号协方差矩阵建模为低秩无噪协方差及稀疏噪声协方差矩阵之和;而后基于低秩恢复理论,构造关于信号和噪声协方差矩阵的凸优化问题;再者构建关于采样协方差矩阵估计误差的凸模型,并将此凸集显式包含进凸优化问题以改善信号协方差矩阵估计性能进而提高DOA估计精度及稳健性;最后基于所得最优无噪声协方差矩阵,利用最小方差无畸变响应(MVDR)方法实现DOA估计。此外,基于采样协方差矩阵估计误差服从渐进正态分布的统计特性,该文推导了一种误差参数因子选取准则以较好重构无噪声协方差矩阵。数值仿真表明,与传统常规波束形成(CBF)、最小方差无畸变响应(MVDR)、传统多重信号分类(MUSIC)及基于稀疏低秩分解的增强拉格朗日乘子(SLD-ALM)算法相比,有限次采样条件下所提算法具有较高DOA估计精度及较好稳健性能。

English

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  • 图 1  有限次快拍条件下邻近非相干信号空域谱

    图 2  非相干信号空域谱

    图 3  估计均方根误差变化曲线

    图 4  平均输出RMSE随SNR或者快拍数变化

    表 1  误差参数对算法重构性能影响

    误差参数($\eta $)理想${R_{\rm s} }$对角线均值理想$R$对角线均值重构${R_{\rm s} }$对角线均值重构$R$对角线均值
    0.16.32467.41816.31107.3918
    16.32467.33845.97387.0775
    46.32467.32715.22906.2905
    86.32467.30124.18555.2388
    126.32467.22753.09574.1583
    166.32467.32682.12943.1999
    196.32467.37241.31332.4336
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  • 通讯作者:  王洪雁, gglongs@163.com
  • 收稿日期:  2019-04-17
  • 录用日期:  2019-09-27
  • 网络出版日期:  2019-10-14
通讯作者: 陈斌, bchen63@163.com
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