NDLI: Joint 2D direction of arrival estimation using a sparse representation of cross covariance matrix

Content Provider	IET Digital Library
Author	Cui, Wei Guo, Shuxu Li, Yunpeng
Abstract	In this study, the problem of two-dimensional (2D) direction of arrival estimation for uncorrelated signals with an L-shaped array is discussed. The estimated cross covariance matrix (CCM) between the sample data along the x-axis and the z-axis is constructed. The elevation angle of the signal is obtained by finding the sparse coefficients of the estimated CCM based on the discussion of statistical probability distribution of the sample data error. Once the elevation angle is obtained, with the non-coupling between the elevation angle and the azimuth angle, the azimuth angle of each source is estimated by the eigenvalue decomposition of the conjugate transpose matrix of the estimated CCM. The proposed algorithm has better estimation performance in the situation of low signal-to-noise-ratio cases but also the estimated azimuth and elevation angles are paired automatically. The performance of the proposed method is compared with some existing schemes and the effectiveness of the method is demonstrated by computer simulations.
Starting Page	2017
Ending Page	2021
Page Count	5
ISSN	17518628
Volume Number	12
e-ISSN	17518636
Issue Number	Issue 16, Oct (2018)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-com/12/16
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-com.2017.1341
Journal	IET Communications
Publisher Date	2018-05-18
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Algebra Array Signal Processing Azimuth Angle Computer Simulations Conjugate Transpose Matrix Covariance Matrices Direction of Arrival Estimation Eigen Values And Eigen Function Eigenvalue Decomposition Elevation Angle Estimated CCM Joint 2D Direction-of-arrival Estimation Joint Two-dimensional Direction-of-arrival Estimation L-shaped Array Low Signal-to-noise-ratio Cases Signal Processing And Detection Signal Processing Theory Sparse Coefficient Sparse Cross Covariance Matrix Representation Statistical Distribution Statistical Probability Distribution Statistics Uncorrelated Signal X-axis Z-axis
Content Type	Text
Resource Type	Article
Subject	Electrical and Electronic Engineering Computer Science Applications

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