高级搜索

留言板

尊敬的读者、作者、审稿人, 关于本刊的投稿、审稿、编辑和出版的任何问题, 您可以本页添加留言。我们将尽快给您答复。谢谢您的支持!

姓名
邮箱
手机号码
标题
留言内容
验证码

基于稀疏和低秩恢复的稳健DOA估计方法

王洪雁 于若男

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, 2020, 42(3): 589-596. doi: 10.11999/JEIT190263
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, 2020, 42(3): 589-596. doi: 10.11999/JEIT190263
Hongyan WANG, Ruonan YU. Sparse and Low Rank Recovery Based Robust DOA Estimation Method[J]. Journal of Electronics and Information Technology, 2020, 42(3): 589-596. doi: 10.11999/JEIT190263
Citation: Hongyan WANG, Ruonan YU. Sparse and Low Rank Recovery Based Robust DOA Estimation Method[J]. Journal of Electronics and Information Technology, 2020, 42(3): 589-596. doi: 10.11999/JEIT190263

基于稀疏和低秩恢复的稳健DOA估计方法

doi: 10.11999/JEIT190263
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)
详细信息
    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

  • 中图分类号: TN911.7

Sparse and Low Rank Recovery Based Robust DOA Estimation Method

Funds: The National Natural Science Foundation of China(61301258, 61271379), The Postdoctoral Science Foundation of China (2016M590218), The Key Laboratory Foundation (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,提出一种基于稀疏低秩分解(SLRD)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。
  • 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
    下载: 导出CSV
  • [1] GUO Muran, ZHANG Y D, and CHEN Tao. DOA estimation using compressed sparse array[J]. IEEE Transactions on Signal Processing, 2018, 66(15): 4133–4146. doi:  10.1109/TSP.2018.2847645
    [2] ZHENG Guimei. DOA estimation in MIMO radar with non-perfectly orthogonal waveforms[J]. IEEE Communications Letters, 2017, 21(2): 414–417. doi:  10.1109/LCOMM.2016.2622691
    [3] CAPON J. High-resolution frequency-wavenumber spectrum analysis[J]. Proceedings of the IEEE, 1969, 57(8): 1408–1418. doi:  10.1109/PROC.1969.7278
    [4] ZHU Shaohao, YANG Kunde, MA Yuanliang, et al. Robust minimum variance distortionless response beamforming using subarray multistage processing for circular hydrophone arrays[C]. 2016 Techno-Ocean, Kobe, Japan, 2016: 692–696. doi: 10.1109/Techno-Ocean.2016.7890744.
    [5] 李立欣, 白童童, 张会生, 等. 改进的双约束稳健Capon波束形成算法[J]. 电子与信息学报, 2016, 38(8): 2014–2019. doi:  10.11999/JEIT151213

    LI Lixin, BAI Tongtong, ZHANG Huisheng, et al. Improved double constraint robust capon beamforming algorithm[J]. Journal of Electronics &Information Technology, 2016, 38(8): 2014–2019. doi:  10.11999/JEIT151213
    [6] VAN TREES H L. Optimum Array Processing: Part IV of Detection, Estimation and Modulation Theory[M]. New York: Wiley-Interscience, 2002.
    [7] LIAO Bin, GUO Chongtao, HUANG Lei, et al. Matrix completion based direction-of-arrival estimation in nonuniform noise[C]. 2016 IEEE International Conference on Digital Signal Processing, Beijing, China, 2016: 66–69.
    [8] SCHMIDT R. Multiple emitter location and signal parameter estimation[J]. IEEE Transactions on Antennas and Propagation, 1986, 34(3): 276–280. doi:  10.1109/TAP.1986.1143830
    [9] HE Shun, YANG Zhiwei, and LIAO Guisheng. DOA estimation of wideband signals based on iterative spectral reconstruction[J]. Journal of Systems Engineering and Electronics, 2017, 28(6): 1039–1045. doi:  10.21629/JSEE.2017.06.01
    [10] GU Yujie and LESHEM A. Robust adaptive beamforming based on interference covariance matrix reconstruction and steering vector estimation[J]. IEEE Transactions on Signal Processing, 2012, 60(7): 3881–3885. doi:  10.1109/TSP.2012.2194289
    [11] 陈沛, 赵拥军, 刘成城. 基于稀疏重构的共形阵列稳健自适应波束形成算法[J]. 电子与信息学报, 2017, 39(2): 301–308. doi:  10.11999/JEIT160436

    CHEN Pei, ZHAO Yongjun, and LIU Chengcheng. Robust adaptive beamforming algorithm for conformal arrays based on sparse reconstruction[J]. Journal of Electronics &Information Technology, 2017, 39(2): 301–308. doi:  10.11999/JEIT160436
    [12] HU Rui, FU Yuli, CHEN Zhen, et al. Robust DOA estimation via sparse signal reconstruction with impulsive noise[J]. IEEE Communications Letters, 2017, 21(6): 1333–1336. doi:  10.1109/LCOMM.2017.2675407
    [13] HUANG Weibin and LI Hui. An improved DOA estimation algorithm based on sparse reconstruction[C]. The 11th International Symposium on Antennas, Propagation and EM Theory, Guilin, China, 2016: 621–625.
    [14] GU Yujie, GOODMAN N A, HONG Shaohua, et al. Robust adaptive beamforming based on interference covariance matrix sparse reconstruction[J]. Signal Processing, 2014, 96: 375–381. doi:  10.1016/j.sigpro.2013.10.009
    [15] HUANG Lei, ZHANG Jing, XU Xu, et al. Robust adaptive beamforming with a novel interference-plus-noise covariance matrix reconstruction method[J]. IEEE Transactions on Signal Processing, 2015, 63(7): 1643–1650. doi:  10.1109/tsp.2015.2396002
    [16] 韦娟, 计永祥, 牛俊儒. 一种新的稀疏重构的DOA估计算法[J]. 西安电子科技大学学报: 自然科学版, 2018, 45(5): 13–18. doi:  10.3969/j.issn.1001-2400.2018.05.003

    WEI Juan, JI Yongxiang, and NIU Junru. Novel algorithm for DOA estimation based on the sparse reconstruction[J]. Journal of Xidian University:Natural Science, 2018, 45(5): 13–18. doi:  10.3969/j.issn.1001-2400.2018.05.003
    [17] CHEN Yong, WANG Fang, WAN Jianwei, et al. Sparse and low-rank decomposition of covariance matrix for efficient DOA estimation[C]. The 9th IEEE International Conference on Communication Software and Networks, Guangzhou, China, 2017: 957–961.
    [18] WANG Xianpeng, ZHU Yanghui, HUANG Mengxing, et al. Unitary matrix completion-based DOA estimation of noncircular signals in nonuniform noise[J]. IEEE Access, 2019, 7: 73719–73728. doi:  10.1109/ACCESS.2019.2920707
    [19] CANDES E J and PLAN Y. Matrix completion with noise[J]. Proceedings of the IEEE, 2010, 98(6): 925–936. doi:  10.1109/jproc.2009.2035722
    [20] HE Zhenqing, SHI Zhiping, and HUANG Lei. Covariance sparsity-aware DOA estimation for nonuniform noise[J]. Digital Signal Processing, 2014, 28: 75–81. doi:  10.1016/j.dsp.2014.02.013
    [21] BLANCHARD P and BRÜNING E. Constrained Minimization Problems (Method of Lagrange Multipliers)[M]. BLANCHARD P and BRÜNING E. Mathematical Methods in Physics. Cham: Birkhäuser, 2015: 537–546.
    [22] LEE S, YOON Y J, LEE J E, et al. Two-stage DOA estimation method for low SNR signals in automotive radars[J]. IET Radar, Sonar & Navigation, 2017, 11(11): 1613–1619. doi:  10.1049/iet-rsn.2017.0221
    [23] TIAN Ye, SUN Xiaoying, and ZHAO Shishun. DOA and power estimation using a sparse representation of second-order statistics vector and ${\ell _0} $ -norm approximation[J]. Signal Processing, 2014, 105: 98–108. doi:  10.1016/j.sigpro.2014.05.014
    [24] HU Yao, ZHANG Debing, YE Jieping, et al. Fast and accurate matrix completion via truncated nuclear norm regularization[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2013, 35(9): 2117–2130. doi:  10.1109/tpami.2012.271
    [25] MALIOUTOV D, ÇETIN M, and WILLSKY A S. A sparse signal reconstruction perspective for source localization with sensor arrays[J]. IEEE Transactions on Signal Processing, 2005, 53(8): 3010–3022. doi:  10.1109/TSP.2005.850882
    [26] WRIGHT J, GANESH A, RAO S, et al. Robust principal component analysis: Exact recovery of corrupted low-rank matrices[J]. arXiv: 0905.0233, 2009.
    [27] HAN Le and LIU Xiaolan. Convex relaxation algorithm for a structured simultaneous low-rank and sparse recovery problem[J]. Journal of the Operations Research Society of China, 2015, 3(3): 363–379. doi:  10.1007/s40305-015-0089-8
    [28] WANG Xianpeng, WANG Luyun, LI Xiumei, et al. Nuclear norm minimization framework for DOA estimation in MIMO radar[J]. Signal Processing, 2017, 135: 147–152. doi:  10.1016/j.sigpro.2016.12.031
    [29] LUO Xiaoyu, FEI Xiaochao, GAN Lu, et al. Direction-of-arrival estimation using an array covariance vector and a reweighted norm[J]. IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences, 2015, E98.A(9): 1964–1967. doi:  10.1587/transfun.E98.A.1964
    [30] LIAO Bin, GUO Chongtao, and SO H. Direction-of-arrival estimation in nonuniform noise via low-rank matrix decomposition[C]. The 22nd International Conference on Digital Signal Processing, London, UK, 2017: 1–4.
    [31] OTTERSTEN B, STOICA P, and ROY R. Covariance matching estimation techniques for array signal processing applications[J]. Digital Signal Processing, 1998, 8(3): 185–210. doi:  10.1006/dspr.1998.0316
    [32] HORN R A and JOHNSON C R. Matrix Analysis[M]. Cambridge, UK: Cambridge University Press, 1985: 1-162.
    [33] ARLOT S and CELISSE A. A survey of cross-validation procedures for model selection[J]. Statistics Surveys, 2010, 4: 40–79. doi:  10.1214/09-SS054
    [34] DAS A. Theoretical and experimental comparison of off-grid sparse Bayesian direction-of-arrival estimation algorithms[J]. IEEE Access, 2017, 5: 18075–18087. doi:  10.1109/ACCESS.2017.2747153
  • [1] 王洪雁, 张海坤.  动态背景下基于低秩及稀疏分解的动目标检测方法, 电子与信息学报. doi: 10.11999/JEIT190452
    [2] 马慧慧, 陶海红.  稀疏拉伸式L型极化敏感阵列的二维波达方向和极化参数联合估计, 电子与信息学报. doi: 10.11999/JEIT190208
    [3] 汪玲, 朱栋强, 马凯莉, 肖卓.  空间目标卡尔曼滤波稀疏成像方法, 电子与信息学报. doi: 10.11999/JEIT170319
    [4] 欧伟枫, 杨春玲, 戴超.  一种视频压缩感知中两级多假设重构及实现方法, 电子与信息学报. doi: 10.11999/JEIT161142
    [5] 李海洋, 王恒远.  基于TL1范数约束的子空间聚类方法, 电子与信息学报. doi: 10.11999/JEIT170193
    [6] 施孝盼, 洪涛.  基于凸优化的稀疏阵列方向调制信号综合算法研究, 电子与信息学报. doi: 10.11999/JEIT170391
    [7] 李少东, 杨军, 陈文峰, 马晓岩.  基于压缩感知理论的雷达成像技术与应用研究进展, 电子与信息学报. doi: 10.11999/JEIT150874
    [8] 张东伟, 郭英, 张坤峰, 齐子森, 韩立峰, 尚耀波.  多跳频信号频率跟踪与二维波达方向实时估计算法, 电子与信息学报. doi: 10.11999/JEIT151170
    [9] 杨立东, 王晶, 谢湘, 赵毅, 匡镜明.  基于低秩张量补全的多声道音频信号恢复方法, 电子与信息学报. doi: 10.11999/JEIT150589
    [10] 李秀友, 薛永华, 董云龙, 关键.  基于迭代凸优化的恒模波形合成方法, 电子与信息学报. doi: 10.11999/JEIT141593
    [11] 张东伟, 郭英, 齐子森, 侯文林, 张波, 李教.  多跳频信号波达方向与极化状态联合估计算法, 电子与信息学报. doi: 10.11999/JEIT141315
    [12] 杨杰, 廖桂生.  基于空域稀疏性的嵌套MIMO雷达DOA估计算法, 电子与信息学报. doi: 10.3724/SP.J.1146.2013.01900
    [13] 席峰, 陈胜垚, 刘中.  混沌模拟信息转换基于多射法的稀疏信号重构, 电子与信息学报. doi: 10.3724/SP.J.1146.2012.00905
    [14] 罗涛, 关永峰, 刘宏伟, 纠博, 吴梦.  平面阵MIMO雷达发射方向图设计方法, 电子与信息学报. doi: 10.3724/SP.J.1146.2013.00568
    [15] 孙磊, 王华力, 许广杰, 苏勇.  基于稀疏贝叶斯学习的高效DOA估计方法, 电子与信息学报. doi: 10.3724/SP.J.1146.2012.01429
    [16] 陈胜垚, 席峰, 刘中.  基于混沌压缩感知的稀疏时变信号在线估计, 电子与信息学报. doi: 10.3724/SP.J.1146.2011.00620
    [17] 李鹏飞, 张旻, 钟子发, 罗争.  基于空频域稀疏表示的宽频段DOA估计, 电子与信息学报. doi: 10.3724/SP.J.1146.2011.00503
    [18] 刘俊, 刘峥, 刘韵佛.  米波雷达仰角和多径衰减系数联合估计算法, 电子与信息学报. doi: 10.3724/SP.J.1146.2010.00251
    [19] 李鹏飞, 张旻, 钟子发.  基于空间角稀疏表示的二维DOA估计, 电子与信息学报. doi: 10.3724/SP.J.1146.2011.00255
    [20] 寇波, 江海, 刘磊, 张冰尘.  基于压缩感知的SAR抑制旁瓣技术研究, 电子与信息学报. doi: 10.3724/SP.J.1146.2010.00103
  • 加载中
  • 图(4) / 表(1)
    计量
    • 文章访问数:  1659
    • HTML全文浏览量:  739
    • PDF下载量:  94
    • 被引次数: 0
    出版历程
    • 收稿日期:  2019-04-17
    • 修回日期:  2019-09-27
    • 网络出版日期:  2019-10-14
    • 刊出日期:  2020-03-19

    目录

      /

      返回文章
      返回

      官方微信,欢迎关注