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基于分布式压缩感知的宽带欠定信号DOA估计

蒋莹 王冰切 韩俊 何翼

蒋莹, 王冰切, 韩俊, 何翼. 基于分布式压缩感知的宽带欠定信号DOA估计[J]. 电子与信息学报, 2019, 41(7): 1690-1697. doi: 10.11999/JEIT180723
引用本文: 蒋莹, 王冰切, 韩俊, 何翼. 基于分布式压缩感知的宽带欠定信号DOA估计[J]. 电子与信息学报, 2019, 41(7): 1690-1697. doi: 10.11999/JEIT180723
Ying JIANG, Bingqie WANG, Jun HAN, Yi HE. Underdetermined Wideband DOA Estimation Based on Distributed Compressive Sensing[J]. Journal of Electronics & Information Technology, 2019, 41(7): 1690-1697. doi: 10.11999/JEIT180723
Citation: Ying JIANG, Bingqie WANG, Jun HAN, Yi HE. Underdetermined Wideband DOA Estimation Based on Distributed Compressive Sensing[J]. Journal of Electronics & Information Technology, 2019, 41(7): 1690-1697. doi: 10.11999/JEIT180723

基于分布式压缩感知的宽带欠定信号DOA估计

doi: 10.11999/JEIT180723
基金项目: 国家自然科学基金(61771484),湖北省自然科学基金(2016CFB288)
详细信息
    作者简介:

    蒋莹:女,1991年生,博士生,研究方向为电子对抗信息处理

    王冰切:男,1972年生,副教授,硕士生导师,研究方向为雷达系统与雷达对抗

    韩俊:男,1983年生,讲师,研究方向为雷达对抗

    何翼:男,1989年生,助理研究员,研究方向为视频图像处理及模式识别与人工智能

    通讯作者:

    何翼 jty614@163.com

  • 中图分类号: TN971

Underdetermined Wideband DOA Estimation Based on Distributed Compressive Sensing

Funds: The National Natural Science Foundation of China(61771484), The Natural Science Foundation of Hubei Province (2016CFB288)
  • 摘要: 为解决基于稀疏阵列的宽带欠定信号到达角(DOA)估计问题,该文提出基于分布式压缩感知(DCS)的宽带DOA估计算法。首先,对稀疏阵列宽带信号处理模型进行理论推导与分析,将宽带信号DOA估计建模成DCS问题;其次,利用经典DCS算法实现稀疏阵列上的宽带欠定信号DOA估计;最后,引入网格失配误差,建立包含网格失配参数的DCS模型,并进行迭代求解,实现对DOA和网格失配参数的联合估计。仿真结果表明,该算法能够实现宽带欠定信号DOA估计,较现有成果而言,在保证测向精度的同时,具备分辨率高、运算速度快的优点。
  • 图  1  CACIS型互质阵列结构

    图  2  SS-MUSIC, DCS-SOMP和DCS-JSOMP算法的空间谱

    图  3  信噪比变化对测向精度的影响

    图  4  频域快拍次数变化对测向精度的影响

    图  5  到达角临近信号估计能力

    表  1  DCS-SOMP算法

     输入:虚拟阵列接收数据${{\text{z}}_h}$,过完备字典集${\text{Φ}_h}\left( \psi \right)$,信号个数$K$。
     输出:重构信号${{\text{s}}_h}$,支撑基列标集合$\varOmega$。
     初始化:迭代计数$i = 1,{\varOmega_0}=\varnothing ,{\hat {\text{s}}_h} = {\text{0}}$,残差初值${{\text{r}}_{h, 0}} = {{\text{z}}_h}$。
     步骤 1  支撑基选择:
    $ {g_i} = \mathop {\arg \max }\limits_{g \in \left\{ {1, 2, \cdots , G} \right\}} \sum\limits_{h = 1}^H {\frac{{\left| {\left\langle {{{\text{r}}_{h, i - 1}}, {{\text{φ}} _{h, g}}} \right\rangle } \right|}}{{{{\left\| {{{\text{φ}} _{h, g}}} \right\|}_2}}}} ,{\varOmega_i}={\varOmega_{i - 1}} \cup \left\{ {{g_i}} \right\}$;
     步骤 2  残差更新:${\hat{\text{ s}}_h}={{\text{Φ}} _{{\varOmega_i}}}^\dagger {{\text{z}}_h},{{\text{r}}_{h, i}} = {{\text{z}}_h} - {{\text{Φ}} _{{\varOmega_i}}}{\hat {\text{s}}_h}$;
     步骤 3  条件判断:若$i < K$,则$i = i + 1$跳至步骤1,否则跳至步
    骤4;
     步骤 4  结果结算:$\varOmega={\varOmega_i},{{\text{s}}_h}={{\text{Φ}} _\varOmega}^\dagger {{\text{z}}_h}$。
    下载: 导出CSV

    表  2  DCS-JSOMP算法

     输入:虚拟阵列接收数据${{\text{z}}_h}$,过完备字典集${\text{Φ}_h}\left( \text{Ψ} \right)$,网格失配字典${\text{Γ}_h}\left( \text{Ψ} \right)$,信号个数$K$。
     输出:重构信号${{\text{s}}_h}$,支撑基列标集合$\varOmega$,网格失配误差${\text{Δ}} $。
     初始化:迭代计数$i = 1,{\varOmega_0}=\varnothing,{\hat{\text{ s}}_h} = {\text{0}},\hat{\text{β}}_h={\text{0}}$,残差${{\text{r}}_{h, 0}} = {{\text{z}}_h}$。
     步骤 1  支撑基选择:${c_g} = \sum\limits_{h = 1}^H {\frac{{\left| {\left\langle {{{\text{r}}_{h, i - 1}}, {{\text{φ }}_{h, g}}} \right\rangle } \right|}}{{{{\left\| {{{\text{φ}} _{h, g}}} \right\|}_2}}}} ,{d_g} = \sum\limits_{h = 1}^H {\frac{{\left| {\left\langle {{{\text{r}}_{h, i - 1}}, {\text{γ}_{h, g}}} \right\rangle } \right|}}{{{{\left\| {{\text{γ}_{h, g}}} \right\|}_2}}}} ,{g_i} = \mathop {\arg \max }\limits_{g \in \left\{ {1, 2, \cdots , G} \right\}} \sqrt {{c_g}^2 + {d_g}^2} ,{\varOmega_i}={\varOmega_{i - 1}} \cup \left\{ {{g_i}} \right\}$;
     步骤 2  残差更新:${\hat{\text{ s}}_h} = {{\text{Φ}} _{{\varOmega_i}}}^\dagger \left( {{{\text{z}}_h} - {{\text{Γ}} _{{\varOmega_i}}}{\hat{\text{β}}_h}} \right),{\hat{\text{β}}_h} = {{\text{Γ}} _{{\varOmega_i}}}^\dagger \left( {{{\text{z}}_h} - {{\text{Φ}} _{{\varOmega_i}}}{{\hat {\text{s}}}_h}} \right),{{\text{r}}_{h, i}} = {{\text{z}}_h} - {{\text{Φ}} _{{\varOmega_i}}}{\hat{\text{ s}}_h} - {{\text{Γ}} _{{\varOmega_i}}}{\hat{\text{β}}_h}$;
     步骤 3  条件判断:若$i < K$,则$i = i + 1$跳至步骤1,否则跳至步骤4;
     步骤 4  结果结算:$\varOmega={\varOmega_i},{{\text{s}}_h} = {{\text{Φ}} _\varOmega}^\dagger \left( {{{\text{z}}_h} - {{\text{Γ}} _\varOmega}{\hat{\text{β}}_h}} \right),{\text{β}_h} = {{\text{Γ}} _\varOmega}^\dagger \left( {{{\text{z}}_h} - {{\text{Φ}} _\varOmega}{{\text{s}}_h}} \right),{\text{Δ}} =\frac{1}{H}\sum\limits_{h = 1}^H {\frac{{{{\text{β}} _h}}}{{{{\text{s}}_h}}}} $。
    下载: 导出CSV

    表  3  5种算法单次蒙特卡洛实验用时(s)

    算法信噪比变化频域快拍次数变化
    DCS-SOMP0.17470.1943
    DCS-JSOMP0.34390.3784
    SS-MUSIC0.50210.5207
    WNNSBL3.37513.0231
    OGSLIM0.60680.6678
    下载: 导出CSV
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出版历程
  • 收稿日期:  2018-07-18
  • 修回日期:  2019-01-11
  • 网络出版日期:  2019-01-22
  • 刊出日期:  2019-07-01

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