基于加权1范数稀疏信号重建的DoA估计

doi:10.13190/j.jbupt.2016.05.007

北京邮电大学学报 ›› 2016, Vol. 39 ›› Issue (5): 33-36,66.doi: 10.13190/j.jbupt.2016.05.007

基于加权1范数稀疏信号重建的DoA估计

赵季红^1,2, 马兆恬¹, 曲桦², 王伟华², 李雷雷¹

1. 西安邮电大学通信与信息工程学院, 西安 710121;
2. 西安交通大学电子与信息工程学院, 西安 710054

收稿日期:2016-03-21 出版日期:2016-10-28 发布日期:2016-12-02
作者简介:赵季红(1963-),女,教授,博士生导师;马兆恬(1991-),女,硕士生,E-mail:wasabi603303961@163.com.
基金资助:
国家自然科学基金项目（61372092）

DoA Estimation Based on Sparse Signal Recovery Utilizing Weighted 1 Norm

ZHAO Ji-hong^1,2, MA Zhao-tian¹, QU Hua², WANG Wei-hua², LI Lei-lei¹

1. School of Communication and Information Engineering, Xi'an University of Posts and Telecommunications, Xi'an 710121, China;
2. The School of Electronic and Information Engineering, Xi'an Jiaotong University, Xi'an 710054, China

Received:2016-03-21 Online:2016-10-28 Published:2016-12-02

摘要/Abstract

摘要： 针对l₁范数下奇异值分解的l₁-SVD稀疏信号重建的波达方向估计方法存在求解量的稀疏性较差且空间谱中存在较多的伪峰，不能准确估计波达方向的问题，对接收信号矩阵进行预处理，并使用信号子空间设计权值矢量得到更好的稀疏性和更好地逼近l₀范数，利用得到的权值矢量对l₁-SVD算法中解矢量的各个元素进行加权，以得到的加权l₁范数作为最小化的目标函数进行优化.仿真结果表明，提出的算法在快拍数、正则化参数和信噪比等条件改变的情况下能有效抑制伪峰，并准确稳定地估计出波达方向.

关键词: 波达方向估计, 矩阵预处理, 稀疏重构, 凸优化

Abstract: When l₁-SVD algorithm is used for direction of arrival (DoA) estimation with few snapshots and low signal noise ratio (SNR), there appear pseudo peaks in spatial spectrum, which lead to inaccurately DoA estimation results. To deal with this problem, the article proposes a DoA estimation method based on sparse signal recovery utilizing weighted 1 norm. Based on the sparse recovery, the weight vector is chosen from the signal subspace and received signal matrix is multiplied. Then the product between weights and elements in the result is made to get weighted 1 norm is taken as target function for minimization. Simulations demonstrate that the proposed algorithm could effectively restrain the pseudo peak and accurately estimate the DoA under the different snapshots, regularization parameter and SNR.

Key words: direction of arrival estimation, matrix pretreatment, sparse reconstruction, convex optimization

中图分类号:

TN929.53

赵季红, 马兆恬, 曲桦, 王伟华, 李雷雷. 基于加权1范数稀疏信号重建的DoA估计[J]. 北京邮电大学学报, 2016, 39(5): 33-36,66.

ZHAO Ji-hong, MA Zhao-tian, QU Hua, WANG Wei-hua, LI Lei-lei. DoA Estimation Based on Sparse Signal Recovery Utilizing Weighted 1 Norm[J]. JOURNAL OF BEIJING UNIVERSITY OF POSTS AND TELECOM, 2016, 39(5): 33-36,66.

参考文献

[1] Ziskind I, Wax M. Maximum likelihood localization of multiple source by alternating projection[J]. IEEE Transaction on Acoustics, Speech and Signal Processing, 1998, 36(10): 1533-1560.
[2] Stoica P, Sharman K C. Maximum likelihood methods for direction-of-arrival estimation[J]. IEEE Transactions on Acoustics, Speech and Signal Processing, 1990, 38(7): 1132-1143.
[3] Swindlehurst A, Kailath T. A performance analysis of subspace-baced methods in the presence of model errors, part I: the MUSIC algorithm[J]. IEEE Transactions on Signal Processing, 1992, 40(7): 1758-1774.
[4] Roy R, 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.
[5] Fuchs J J. Identification of real sinusoids in noise, the global matched filter approach[J]. 42^nd Asilomar Conference Signals, System and Computers, Pacific Grove, CA, 2008: 269-273.
[6] Malioutov D, Cetin M, Willsky A S. A sparce signal reconstruction perspective for source localization with sensor arrays[J]. IEEE Transactions on Signal Processing, 2005, 53(8): 3010-3022.
[7] Zheng Chundi, Li Gang, Zhang Hao, et al. An approach of DoA estimation using subspace weighted l₁ minimization[J]. IEEE International Conference on Acoustics, Speech and Signal Processing, 2011: 2856-2859.
[8] 韩树楠, 李东生, 张浩, 等. 基于加权1范数的稀疏重构波达方向估计算法[J]. 探测与控制学报, 2015, 37(2): 82-85. Han Shunan, Li Dongsheng, Zhang Hao, et al. A DoA estimation algorithm based on weighted 1 norm through sparse reconstruction[J]. Journal of Detection and Control, 2015, 37(2): 82-85.
[9] Lobo M, Vandenberghe L, Boyd S, et al. Applications of second-order cone programming[J]. Linear Algebra Application, Special Issue on Linear Algebra on Control, Signals, and Image Processing, 1998: 193-228.
[10] Grant M, Boyd S. CVX: Matlab software for disciplined convex programming[EB/OL]. 2014. http://cvxr.com/cvx.2014.
[11] Xu Xu, Wei Xiaohan, Ye Zhongfu. DoA estimation based on sparse signal recovery utilizing weighted l₁-norm penalty[J]. IEEE Signal Processing Letters, 2012, 19(3): 155-157.
[12] Rahmat S, Nurul H N, Ahmed O E R, et al. Capon-like DoA estimation algorithm for directional antenna arrays[J]. Loughborough Antenna and Propagation Conference, UK, 2011: 1-4.

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基于加权1范数稀疏信号重建的DoA估计

DoA Estimation Based on Sparse Signal Recovery Utilizing Weighted 1 Norm

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