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基于稀疏贝叶斯学习的空间紧邻信号DOA估计算法

王琦森 余华 李杰 董超 季飞 陈焱琨

王琦森, 余华, 李杰, 董超, 季飞, 陈焱琨. 基于稀疏贝叶斯学习的空间紧邻信号DOA估计算法[J]. 电子与信息学报, 2021, 43(3): 708-716. doi: 10.11999/JEIT200656
引用本文: 王琦森, 余华, 李杰, 董超, 季飞, 陈焱琨. 基于稀疏贝叶斯学习的空间紧邻信号DOA估计算法[J]. 电子与信息学报, 2021, 43(3): 708-716. doi: 10.11999/JEIT200656
Qisen WANG, Hua YU, Jie LI, Chao DONG, Fei JI, Yankun CHEN. Sparse Bayesian Learning Based Algorithm for DOA Estimation of Closely Spaced Signals[J]. Journal of Electronics and Information Technology, 2021, 43(3): 708-716. doi: 10.11999/JEIT200656
Citation: Qisen WANG, Hua YU, Jie LI, Chao DONG, Fei JI, Yankun CHEN. Sparse Bayesian Learning Based Algorithm for DOA Estimation of Closely Spaced Signals[J]. Journal of Electronics and Information Technology, 2021, 43(3): 708-716. doi: 10.11999/JEIT200656

基于稀疏贝叶斯学习的空间紧邻信号DOA估计算法

doi: 10.11999/JEIT200656
基金项目: 国家自然科学基金(U1809211, 61771202, 61971198),广东省海洋经济发展专项资金重点项目(粤自然资合[2020]009号),广东省基础与应用基础研究基金(2019A151501104),自然资源部海洋环境探测技术与应用重点实验室开放基金课题(MESTA-2020-A005)
详细信息
    作者简介:

    王琦森:男,1996年生,博士生,研究方向为水声信号处理

    余华:男,1973年生,教授,研究方向为无线通信与网络、水声通信网络、水声信号处理等

    李杰:男,1984 年生,副研究员,研究方向为阵列信号处理、水声通信等

    董超:男,1982年生,副研究员,研究方向为海洋无人智能装备

    季飞:女,1970年生,教授,研究方向为无线通信与网络、水声通信等

    陈焱琨:女,1982年生,工程师,研究方向为水声信号处理

    通讯作者:

    李杰 eejli@scut.edu.cn

  • 1)算法matlab代码在链接:https://pan.baidu.com/s/1JkWTe1JxPSszPpGAik6Fmg 提取码:MSBL
  • 中图分类号: TN911.72

Sparse Bayesian Learning Based Algorithm for DOA Estimation of Closely Spaced Signals

Funds: The National Natural Science Foundation of China (U1809211, 61771202, 61971198), The Key Program of Marine Economy Development Special Foundation of Guangdong Province (GDNRC [2020]009), Guangdong Basic and Applied Basic Research Foundation (2019A151501104), Open Funding Project of Key Laboratory of Marine Environmental Survey Technology and Application, Ministry of Natural Resources (MESTA-2020-A005)
  • 摘要: 离格(off-grid)波达方向(DOA)估计解决的是实际DOA和假设网格点的失配问题。对于空间紧邻信号的DOA,稀疏的网格点会导致精度和分辨率的下降,密集的网格点虽然可以提高估计精度却显著增加计算负担。针对此问题,该文提出基于稀疏贝叶斯学习(SBL)的空间紧邻信号DOA估计算法,主要包括3个步骤。首先,通过最大化阵列输出的边缘似然函数,推导了信号在拉普拉斯先验下的新不动点迭代方法,进行超参数的预估计,相比其他经典SBL算法提高了收敛速度;其次,利用新网格插值方法优化网格点集,并二次估计噪声方差和信号功率以分辨空间紧邻信号的DOA;最后,推导了似然函数关于角度的最大化公式以改进离格DOA搜索。仿真表明该算法比其他经典SBL类算法对空间紧邻信号的DOA具有更高的精度和分辨率,同时有计算效率的提升。
    1)  1)算法matlab代码在链接:https://pan.baidu.com/s/1JkWTe1JxPSszPpGAik6Fmg 提取码:MSBL
  • 图  1  网格点插值优化

    图  2  算法的空间谱对比

    图  3  不同信噪比下的估计性能和运算效率

    图  4  不同DOA间隔下的分辨能力比较

    图  5  不同迭代门限下的估计精度和计算复杂度

    表  1  算法的计算复杂度

    算法计算复杂度
    本文GI-MSBL$\begin{gathered} {l_1}\left( {2MNL + (M + 1)MN} \right) \\ + {l_2}\left( {M\bar NL + (M + 1)M\bar N} \right) + (2{M^2} + 2M)K{N_0} \\ \end{gathered} $
    iRVM-DOA[7]$\begin{gathered} {l_{{\rm{iRVM - DOA}}}}\left( {MNL + M{N^2} + {M^2}N} \right) \\ + (2{M^2}{\rm{ + }}6M)K{N_1} \\ \end{gathered} $
    OGSBI[8]${l_{{\rm{OGSBI}}}}\left( {2MNL + M{N^2} + {M^2}N + MN(L + K)} \right)$
    rootSBL[12]${l_{{\rm{rootSBL}}}}\left( {2MNL + M{N^2} + {M^2}N{\rm{ + }}M(N - 1)L} \right)$
    PSBL[9,10]$ \begin{array}{l}{l}_{\rm{PSBL}}\left(2MNL+M(N-1{)}^{2}+{M}^{2}(N-1)\right)\\ +{l}_{\rm{PSBL}}\left(L(N-1)(M+K)\right)\end{array}$
    L1-SVD[3]$O({(KN)^3})$
    MUSIC[2]$(M - K)ML{N_{{\rm{MUSIC}}}}$
    下载: 导出CSV
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出版历程
  • 收稿日期:  2020-08-03
  • 修回日期:  2021-01-25
  • 网络出版日期:  2021-02-04
  • 刊出日期:  2021-03-22

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