A covariance matrix shrinkage method with Toeplitz rectified target for DOA estimation under the uniform linear array

被引:22
作者
Liu, Yanyan [1 ]
Sun, Xiaoying [1 ]
Zhao, Shishun [2 ]
机构
[1] Jilin Univ, Dept Commun Engn, Changchun 130025, Jilin, Peoples R China
[2] Jilin Univ, Dept Math, Changchun 130022, Jilin, Peoples R China
基金
中国国家自然科学基金;
关键词
Direction of arrival (DOA) estimation; Covariance matrix estimation; Shrinkage estimation; MUSIC; Random matrix theory; WISHART DISTRIBUTION; ALGORITHMS;
D O I
10.1016/j.aeue.2017.06.026
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
A covariance matrix shrinkage method is proposed to make an improvement of the direction of arrival (DOA) estimation under a uniform linear array in a scenario where the number of sensors is large and the sample size is relatively small. The main contribution is that we provide a shrinkage target with Toeplitz structure and deduce a closed-form estimation of the shrinkage coefficient. The closed-form and the expectation of the shrinkage coefficient estimate are calculated based on the unbiased and consistent estimates of the trace and moments of a Wishart distributed covariance matrix. The statistical property of the shrinkage coefficient estimate is discussed through theoretical analysis and simulations, which demonstrate the shrinkage coefficient estimate can ensure that the proposed covariance matrix estimate is a good compromise between the sample covariance matrix (SCM) and the target. The root mean-square-error (RMSE) simulations of DOA estimation show that the proposed method can improve the multiple signal classification (MUSIC) DOA estimation performance in the case of low signal-to-noise ratio (SNR) with small sample size, and also can provide a satisfactory performance at high SNR. (C) 2017 Published by Elsevier GmbH.
引用
收藏
页码:50 / 55
页数:6
相关论文
共 20 条
  • [1] On the strong convergence of the optimal linear shrinkage estimator for large dimensional covariance matrix
    Bodnar, Taras
    Gupta, Arjun K.
    Parolya, Nestor
    [J]. JOURNAL OF MULTIVARIATE ANALYSIS, 2014, 132 : 215 - 228
  • [2] DOA estimation in the time-space CDMA system using modified particle swarm optimization
    Chang, Jhih-Chung
    Chang, Ann-Chen
    [J]. AEU-INTERNATIONAL JOURNAL OF ELECTRONICS AND COMMUNICATIONS, 2013, 67 (04) : 340 - 347
  • [3] Shrinkage-to-Tapering Estimation of Large Covariance Matrices
    Chen, Xiaohui
    Wang, Z. Jane
    McKeown, Martin J.
    [J]. IEEE TRANSACTIONS ON SIGNAL PROCESSING, 2012, 60 (11) : 5640 - 5656
  • [4] Chen Y., 2010, SENSOR ARRAY MULTICH, V59, P4097
  • [5] Shrinkage Algorithms for MMSE Covariance Estimation
    Chen, Yilun
    Wiesel, Ami
    Eldar, Yonina C.
    Hero, Alfred O.
    [J]. IEEE TRANSACTIONS ON SIGNAL PROCESSING, 2010, 58 (10) : 5016 - 5029
  • [6] DOA estimation of LFM signals based on STFT and multiple invariance ESPRIT
    Cui, Kaibo
    Wu, Weiwei
    Huang, Jingjian
    Chen, Xi
    Yuan, Naichang
    [J]. AEU-INTERNATIONAL JOURNAL OF ELECTRONICS AND COMMUNICATIONS, 2017, 77 : 10 - 17
  • [7] Generalized rectification of cross spectral matrices for arrays of arbitrary geometry
    Forster, P
    [J]. IEEE TRANSACTIONS ON SIGNAL PROCESSING, 2001, 49 (05) : 972 - 978
  • [8] Graczyk P, 2003, ANN STAT, V31, P287
  • [9] Comparison of linear shrinkage estimators of a large covariance matrix in normal and non-normal distributions
    Ikeda, Yuki
    Kubokawa, Tatsuya
    Srivastava, Muni S.
    [J]. COMPUTATIONAL STATISTICS & DATA ANALYSIS, 2016, 95 : 95 - 108
  • [10] MUSIC, G-MUSIC, and maximum-likelihood performance breakdown
    Johnson, Ben A.
    Abramovich, Yuri I.
    Mestre, Xavier
    [J]. IEEE TRANSACTIONS ON SIGNAL PROCESSING, 2008, 56 (08) : 3944 - 3958