高级搜索

适用于二维阵列的无格稀疏波达方向估计算法

王剑书 樊养余 杜瑞 吕国云

引用本文: 王剑书, 樊养余, 杜瑞, 吕国云. 适用于二维阵列的无格稀疏波达方向估计算法[J]. 电子与信息学报, 2019, 41(2): 447-454. doi: 10.11999/JEIT180340 shu
Citation:  Jianshu WANG, Yangyu FAN, Rui DU, Guoyun LÜ. Gridless Sparse Method for Direction of Arrival Estimation for Two-dimensional Array[J]. Journal of Electronics and Information Technology, 2019, 41(2): 447-454. doi: 10.11999/JEIT180340 shu

适用于二维阵列的无格稀疏波达方向估计算法

    作者简介: 王剑书: 男,1989年生,博士生,研究方向为阵列信号处理、DOA估计和波束形成等;
    樊养余: 男,1960年生,教授,主要研究方向为数字图像处理、数字信号处理理论与应用、无线光通信技术和虚拟现实技术等;
    杜瑞: 男,1988年生,博士生,研究方向为雷达信号处理和模式识别等;
    吕国云: 男,1975年生,副教授,主要研究方向为信号与信息处理、语音和图像处理、虚拟现实和嵌入式系统和高速信号处理等
    通讯作者: 王剑书,wangjs123@mail.nwpu.edu.cn
  • 基金项目: 水声对抗重点实验室基金(kmb5494)

摘要: 针对现有的适用于2维阵列的无格稀疏波达方向(DOA)估计方法性能不足的问题,该文提出一种新的方法。对2维阵列,从原子L0范数出发,证明其值等于一个以矩阵秩为目标函数的半定规划(SDP)问题的最优解。对该矩阵使用第1类有限阶贝塞尔函数近似表达,构造新的秩优化SDP问题。根据低秩矩阵恢复理论,对该SDP问题的目标函数使用log-det函数方法平滑替代,然后使用优化最小(MM)算法求解,最后通过(半)正定Toeplitz矩阵的范德蒙分解方法实现无格DOA估计。在MM算法求解模型时,使用样本协方差矩阵构造初始优化问题,减少算法迭代。仿真实验结果表明,相较于基于网格的MUSIC和其他无格DOA估计方法,该文方法具有更好的均方根误差(RMSE)性能与对相邻源的分辨能力;在快拍数充足且信噪比(SNR)较高时,适当的第1类贝塞尔函数阶数选择可以实现与较大阶数接近的RMSE性能,同时能减少运行时间。

English

    1. [1]

      QIN Si, ZHANG Yimin D, and AMIN M G. DOA estimation of mixed coherent and uncorrelated targets exploiting coprime MIMO radar[J]. Digital Signal Processing, 2017, 61: 26–34. doi: 10.1016/j.dsp.2016.06.006

    2. [2]

      SAUCAN A A, CHONAVEL T, SINTES C, et al. CPHD-DOA tracking of multiple extended sonar targets in impulsive environments[J]. IEEE Transactions on Signal Processing, 2016, 64(5): 1147–1160. doi: 10.1109/TSP.2015.2504349

    3. [3]

      HE Saijuan and CHEN Huawei. Closed-form DOA estimation using first-order differential microphone arrays via joint temporal-spectral-spatial processing[J]. IEEE Sensors Journal, 2017, 17(4): 1046–1060. doi: 10.1109/JSEN.2016.2641449

    4. [4]

      WAN Liangtian, HAN Guangjie, JIANG Jinfang, et al. A DOA estimation approach for transmission performance guarantee in D2D communication[J]. Mobile Networks and Applications, 2017, 22(6): 998–1009. doi: 10.1007/s11036-017-0820-2

    5. [5]

      CAPON J. High-resolution frequency-wavenumber spectrum analysis[J]. Proceedings of the IEEE, 1969, 57(8): 1408–1418. doi: 10.1109/PROC.1969.7278

    6. [6]

      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

    7. [7]

      ROY R and KAILATH T. ESPRIT—Estimation of signal parameters via rotational invariance techniques[J]. IEEE Transactions on Acoustics, Speech, and Signal Processing, 1989, 37(7): 984–995. doi: 10.1109/29.32276

    8. [8]

      MALIOUTOV D, CETIN 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

    9. [9]

      WIPF D P and RAO B D. An empirical Bayesian strategy for solving the simultaneous sparse approximation problem[J]. IEEE Transactions on Signal Processing, 2007, 55(7): 3704–3716. doi: 10.1109/TSP.2007.894265

    10. [10]

      LIU Zhangmeng, HUANG Zhitao, and ZHOU Yiyu. An efficient maximum likelihood method for direction-of-arrival estimation via sparse Bayesian learning[J]. IEEE Transactions on Wireless Communications, 2012, 11(10): 1–11. doi: 10.1109/TWC.2012.090312.111912

    11. [11]

      BHASKAR B N, TANG Gongguo, and RECHT B. Atomic norm denoising with applications to line spectral estimation[J]. IEEE Transactions on Signal Processing, 2013, 61(23): 5987–5999. doi: 10.1109/TSP.2013.2273443

    12. [12]

      QIAN Tong, TIAN Jing, ZHANG Xu, et al. Atomic norm method for DOA estimation in random sampling condition[C]. 2016 CIE International Conference on Radar (RADAR), Guangzhou, China, 2016: 1–4.

    13. [13]

      ZHANG Yu, ZHANG Gong, and WANG Xinhai. Array covariance matrix-based atomic norm minimization for off-grid coherent direction-of-arrival estimation[C]. 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), New Orleans, USA, 2017: 3196–3200. doi: 10.1109/ICASSP.2017.7952746.

    14. [14]

      CHEN Yuxin and CHI Yuejie. Robust spectral compressed sensing via structured matrix completion[J]. IEEE Transactions on Information Theory, 2014, 60(10): 6576–6601. doi: 10.1109/TIT.2014.2343623

    15. [15]

      YANG Zai, LI Jian, STOICA P, et al. Sparse methods for direction-of-arrival estimation[OL]. http://cn.arxiv.org/pdf/1609.09596v2, 2017.3.

    16. [16]

      YANG Zai, XIE Lihua, and ZHANG Cishen. A discretization-free sparse and parametric approach for linear array signal processing[J]. IEEE Transactions on Signal Processing, 2014, 62(19): 4959–4973. doi: 10.1109/TSP.2014.2339792

    17. [17]

      YANG Zai and XIE Lihua. On gridless sparse methods for multi-snapshot direction of arrival estimation[J]. Circuits, Systems, and Signal Processing, 2017, 36(8): 3370–3384. doi: 10.1007/s00034-016-0462-9

    18. [18]

      ZHANG Youwen, HONG Xiaoping, WANG Yonggang, et al. Gridless SPICE applied to parameter estimation of underwater acoustic frequency hopping signals[C]. 2016 IEEE/OES Chian Ocean Acoustics (COA), Harbin, China, 2016: 1–6.

    19. [19]

      MAHATA K and HYDER M M. Grid-less TV minimization for DOA estimation[J]. Signal Processing, 2017, 132: 155–164. doi: 10.1016/j.sigpro.2016.09.018

    20. [20]

      RAO B D and HARI K V S. Performance analysis of root-MUSIC[J]. IEEE Transactions on Acoustics, Speech, and Signal Processing, 1989, 37(12): 1939–1949. doi: 10.1109/29.45540

    21. [21]

      TANG Gongguo, BHASKAR B N, SHAH P, et al. Compressed sensing off the grid[J]. IEEE Transactions on Information Theory, 2013, 59(11): 7465–7490. doi: 10.1109/TIT.2013.2277451

    22. [22]

      MOHAN K and FAZEL M. Iterative reweighted algorithms for matrix rank minimization[J]. Journal of Machine Learning Research, 2012, 13(11): 3441–3473.

    23. [23]

      SUNDIN M, ROJAS C R, JANSSON M, et al. Relevance singular vector machine for low-rank matrix reconstruction[J]. IEEE Transactions on Signal Processing, 2016, 64(20): 5327–5339. doi: 10.1109/TSP.2016.2597121

    24. [24]

      SUN Ying, BABU P, and PALOMAR D P. Majorization-minimization algorithms in signal processing, communications, and machine learning[J]. IEEE Transactions on Signal Processing, 2017, 65(3): 794–816. doi: 10.1109/TSP.2016.2601299

    25. [25]

      HORN R A and JOHNSON C R. Matrix Analysis[M]. Cambridge: Cambridge University Press, 2013: 495–497.

    26. [26]

      LANDAU H J. The classical moment problem: Hilbertian proofs[J]. Journal of Functional Analysis, 1980, 38(2): 255–272. doi: 10.1016/0022-1236(80)90065-8

    27. [27]

      STOICA P and MOSES R L. Spectral Analysis of Signals[M]. Upper Saddle River, NJ: Pearson Prentice Hall, 2005: 172–177.

    28. [28]

      YANG Zai and XIE Lihua. Enhancing sparsity and resolution via reweighted atomic norm minimization[J]. IEEE Transactions on Signal Processing, 2016, 64(4): 995–1006. doi: 10.1109/TSP.2015.2493987

    29. [29]

      FERNANDEZGRANDA C. Super-resolution of point sources via convex programming[C]. IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, Cancun, Mexico, 2015: 41–44.

    30. [30]

      CANDES E J and FERNANDEZ-GRANDA C. Towards a mathematical theory of super-resolution[J]. Communications on Pure and Applied Mathematics, 2014, 67(6): 906–956. doi: 10.1002/cpa.v67.6

    31. [31]

      STURM J F. Using SeDuMi 1.02, a MATLAB toolbox for optimization over symmetric cones[J]. Optimization Methods and Software, 1999, 11(1/4): 625–653. doi: 10.1080/10556789908805766

    32. [32]

      TOH K C, TODD M J, and TUTUNCU R H. SDPT3—A Matlab software package for semidefinite programming, Version 1.3[J]. Optimization Methods and Software, 1999, 11(1/4): 545–581. doi: 10.1080/10556789908805762

    33. [33]

      STURM J F. Implementation of interior point methods for mixed semidefinite and second order cone optimization problems[J]. Optimization Methods and Software, 2002, 17(6): 1105–1154. doi: 10.1080/1055678021000045123

    34. [34]

      STOICA P and NEHORAI A. Performance study of conditional and unconditional direction-of-arrival estimation[J]. IEEE Transactions on Acoustics, Speech, and Signal Processing, 1990, 38(10): 1783–1795. doi: 10.1109/29.60109

    1. [1]

      孙霆, 董春曦, 毛昱. 一种基于半定松弛技术的TDOA-FDOA无源定位算法. 电子与信息学报, 2020, 42(7): 1599-1605.

    2. [2]

      项厚宏, 陈伯孝, 杨婷, 杨明磊. 基于多帧相位增强的米波雷达低仰角目标DOA估计方法. 电子与信息学报, 2020, 42(7): 1581-1589.

    3. [3]

      张天骐, 喻盛琪, 张天, 葛宛营. 基于张量分解和多项式库搜索的多天线NPLC-DS-CDMA伪码序列估计. 电子与信息学报, 2020, 41(0): 1-8.

    4. [4]

      王延峰, 张桢桢, 王盼如, 孙军伟. 基于DNA链置换的两位格雷码减法器分子电路设计. 电子与信息学报, 2020, 42(0): 1-9.

    5. [5]

      熊伟, 顾祥岐, 徐从安, 崔亚奇. 多编队目标先后出现时的无先验信息跟踪方法. 电子与信息学报, 2020, 42(7): 1619-1626.

    6. [6]

      刘坤, 吴建新, 甄杰, 王彤. 基于阵列天线和稀疏贝叶斯学习的室内定位方法. 电子与信息学报, 2020, 42(5): 1158-1164.

    7. [7]

      吕晓德, 孙正豪, 刘忠胜, 张汉良, 刘平羽. 基于二阶统计量盲源分离算法的无源雷达同频干扰抑制研究. 电子与信息学报, 2020, 42(5): 1288-1296.

    8. [8]

      宋广南, 卢海梁, 李浩, 李一楠, 郎量, 董思乔, 李鹏飞, 吕容川. 复杂天气及海风对天基被动干涉微波辐射无源探测系统性能的影响. 电子与信息学报, 2020, 42(0): 1-8.

    9. [9]

      李一楠, 张林让, 卢海梁, 李鹏飞, 吕容川, 李浩, 付庸杰, 邱尔雅, 唐世阳. 基于地基综合孔径微波辐射计的空中目标无源探测技术研究. 电子与信息学报, 2020, 41(0): 1-8.

    10. [10]

      韩冬, 周良将, 焦泽坤, 吴一戎. 基于改进三维后向投影的多圈圆迹SAR相干三维成像方法. 电子与信息学报, 2020, 42(0): 1-7.

    11. [11]

      曹祥红, 李欣妍, 魏晓鸽, 李森, 黄梦溪, 李栋禄. 基于Dijkstra-ACO混合算法的应急疏散路径动态规划. 电子与信息学报, 2020, 42(6): 1502-1509.

    12. [12]

      李劲松, 彭建华, 刘树新, 季新生. 一种基于线性规划的有向网络链路预测方法. 电子与信息学报, 2020, 41(0): 1-9.

    13. [13]

      李伟, 高嘉浩, 杜怡然, 陈韬. 一种密码专用可编程逻辑阵列的分组密码能效模型及其映射算法. 电子与信息学报, 2020, 41(0): 1-9.

    14. [14]

      杨静, 李金科. 带有特征感知的D2D内容缓存策略. 电子与信息学报, 2020, 42(0): 1-7.

    15. [15]

      钱志鸿, 蒙武杰, 王雪, 胡良帅, 王鑫. 全负载蜂窝网络下多复用D2D通信功率分配算法研究. 电子与信息学报, 2020, 41(0): 1-7.

    16. [16]

      臧艺超, 周天阳, 朱俊虎, 王清贤. 领域独立智能规划技术及其面向自动化渗透测试的攻击路径发现研究进展. 电子与信息学报, 2020, 41(0): 1-13.

    17. [17]

      刘新, 阎焜, 杨光耀, 叶盛波, 张群英, 方广有. UWB-MIMO穿墙雷达三维成像与运动补偿算法研究. 电子与信息学报, 2020, 41(0): 1-8.

    18. [18]

      王年, 胡旭阳, 朱凡, 唐俊. 基于视图感知的单视图三维重建算法. 电子与信息学报, 2020, 42(0): 1-8.

    19. [19]

      陈根华, 陈伯孝. 复杂多径信号下基于空域变换的米波雷达稳健测高算法. 电子与信息学报, 2020, 42(5): 1297-1302.

    20. [20]

      裴二荣, 易鑫, 邓炳光, 李金艳, 张蕾. D2D辅助的NB-IoT中能耗和传输成功率的最优折中. 电子与信息学报, 2020, 41(0): 1-8.

  • 图 1  RMSE仿真实验结果

    图 2  相邻源RMSE仿真实验结果

    图 3  不同贝塞尔函数阶数的本文方法仿真实验结果

    表 1  不同贝塞尔函数阶数的本文方法平均运行时间(s)

    N20406080
    运行时间0.74531.75364.03658.0497
    下载: 导出CSV
  • 加载中
图(3)表(1)
计量
  • PDF下载量:  49
  • 文章访问数:  607
  • HTML全文浏览量:  890
文章相关
  • 通讯作者:  王剑书, wangjs123@mail.nwpu.edu.cn
  • 收稿日期:  2018-04-12
  • 录用日期:  2018-09-04
  • 网络出版日期:  2018-09-12
  • 刊出日期:  2019-02-01
通讯作者: 陈斌, bchen63@163.com
  • 1. 

    沈阳化工大学材料科学与工程学院 沈阳 110142

  1. 本站搜索
  2. 百度学术搜索
  3. 万方数据库搜索
  4. CNKI搜索

/

返回文章