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基于原子范数最小化的单比特稀疏双极子阵列的波达角估计

程智勇 陈胜垚 吴文 刘中

程智勇,陈胜垚,吴文,等. 基于原子范数最小化的单比特稀疏双极子阵列的波达角估计[J]. 电波科学学报,xxxx,x(x): x-xx. DOI: 10.12265/j.cjors.2022022
引用本文: 程智勇,陈胜垚,吴文,等. 基于原子范数最小化的单比特稀疏双极子阵列的波达角估计[J]. 电波科学学报,xxxx,x(x): x-xx. DOI: 10.12265/j.cjors.2022022
CHENG Z Y, CHEN S Y, WU W, et al. DOA estimation of one-bit sparse cross-dipole array based on atomic norm minimization[J]. Chinese journal of radio science,xxxx,x(x): x-xx. (in Chinese). DOI: 10.12265/j.cjors.2022022
Citation: CHENG Z Y, CHEN S Y, WU W, et al. DOA estimation of one-bit sparse cross-dipole array based on atomic norm minimization[J]. Chinese journal of radio science,xxxx,x(x): x-xx. (in Chinese). DOI: 10.12265/j.cjors.2022022

基于原子范数最小化的单比特稀疏双极子阵列的波达角估计

doi: 10.12265/j.cjors.2022022
基金项目: 国家自然科学基金(62171224)
详细信息
    作者简介:

    程智勇:(1989—),男,山东人,南京理工大学博士研究生,研究方向为阵列信号处理

    陈胜垚:(1985—),男,安徽人,南京理工大学副教授,研究方向为雷达信号处理,深度学习

    吴文:(1968—),男,江苏人,南京理工大学教授,研究方向为天线理论,微波探测技术

    刘中:(1963—),男,安徽人,南京理工大学教授,研究方向为雷达信号处理,深度学习

    通讯作者:

    陈胜垚 E-mail: chenshengyao@njust.edu.cn

  • 中图分类号: TN911.7

DOA estimation of one-bit sparse cross-dipole array based on atomic norm minimization

  • 摘要: 现有的单比特稀疏双极子阵列的波达角估计方法为子空间方法,其估计精度依赖于信号的统计特征,并且没有充分利用协方差矩阵的结构,导致其估计精度较低。为了提高该阵列的波达角估计精度,本文提出了一种基于原子范数最小化的无网格稀疏化波达角估计方法。该方法将稀疏双极子阵列的波达角估计转化为标量阵波达角估计,并根据参数空间的连续性构造基于原子集的阵列信号稀疏模型,随后利用单比特采样下噪声的稀疏特征,将该波达角估计问题转化为${l_1}$范数约束下的原子范数最小化问题,并且给出一种基于交替方向乘子法的快速迭代求解方法。仿真结果表明:相较于现有的方法,本文所提方法有着更高的估计精度,在嵌套阵上,当信噪比为-5 dB时,其估计精度均方误差降低了17.9 dB;并且将求解原子范数最小化的计算复杂度由$O({N^{6.5}})$降低为$O({N^3})$,其中$N$为与稀疏阵具有相同孔径和相同阵元间距的均匀线阵的阵元个数。
  • 图  1  单比特嵌套双极子阵列

    Fig.  1  One-bit nested cross-dipole array

    图  2  $p(\tilde n = 0)$ 随SNR变化

    Fig.  2  $p(\tilde n = 0)$vs. SNR

    图  3  OB-ADMM的MUSIC谱

    Fig.  3  MUSIC spatial spectrum of OB-ADMM

    图  4  OB-ADMM估计精度MSE随SNR的变化

    Fig.  4  MSE of OB-ADMM vs. SNR

    图  5  OB-ADMM估计精度MSE随快拍数的变化

    Fig.  5  MSE of OB-ADMM vs. number of snapshots

    图  6  不同算法的CPU时间随快拍数变化

    Fig.  6  CPU times vs. number of snapshots with different algorithms

    图  7  不同算法的CPU时间随$N$变化

    Fig.  7  CPU times v.s. $N$

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出版历程
  • 收稿日期:  2022-02-14
  • 录用日期:  2022-05-11
  • 网络出版日期:  2022-05-11

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