scholarly journals A Sparse Representation Method for Coherent Sources Angle Estimation with Uniform Circular Array

2019 ◽  
Vol 2019 ◽  
pp. 1-9
Author(s):  
Xiaolong Su ◽  
Zhen Liu ◽  
Tianpeng Liu ◽  
Bo Peng ◽  
Xin Chen ◽  
...  
Keyword(s):  
Objective Function ◽  
Sparse Representation ◽  
Source Localization ◽  
Doa Estimation ◽  
Spatial Dimension ◽  
Spatial Spectrum ◽  
Circular Array ◽  
Coherent Sources ◽  
Representation Method ◽  
Coherent Source

Coherent source localization is a common problem in signal processing. In this paper, a sparse representation method is considered to deal with two-dimensional (2D) direction of arrival (DOA) estimation for coherent sources with a uniform circular array (UCA). Considering that objective function requires sparsity in the spatial dimension but does not require sparsity in time, singular value decomposition (SVD) is employed to reduce computational complexity and ℓ2 norm is utilized to renew objective function. After the new objective function is constructed to evaluate residual and sparsity, a second-order cone (SOC) programming is employed to solve convex optimization problem and obtain 2D spatial spectrum. Simulations show that the proposed method can deal with the case of coherent source localization, which has higher resolution than 2D MUSIC method and does not need to estimate the number of coherent sources in advance.

Enhanced covariances matrix sparse representation method for DOA estimation

2015 ◽  
Vol 51 (16) ◽  
pp. 1288-1290 ◽  
Author(s):  
Wei Cui ◽  
Tong Qian ◽  
Jing Tian
Keyword(s):  
Sparse Representation ◽  
Doa Estimation ◽  
Representation Method

Efficient sparse representation method for wideband DOA estimation using focusing operation

2017 ◽  
Vol 11 (11) ◽  
pp. 1673-1678 ◽  
Author(s):  
Yonghong Zhao ◽  
Linrang Zhang ◽  
Yabin Gu ◽  
Yumei Guo ◽  
Juan Zhang
Keyword(s):  
Sparse Representation ◽  
Doa Estimation ◽  
Representation Method

Grid-less DOA estimation of coherent sources based on the covariance matrix recovery

2021 ◽  
pp. 101345
Author(s):  
Shuang Wu ◽  
Ye Yuan ◽  
Lei Huang ◽  
Kaibo Cui ◽  
Naichang Yuan
Keyword(s):  
Covariance Matrix ◽  
Doa Estimation ◽  
Matrix Recovery ◽  
Coherent Sources

scholarly journals Spectral Domain Sparse Representation for DOA Estimation of Signals with Large Dynamic Range

Sensors ◽  
2021 ◽  
Vol 21 (15) ◽  
pp. 5164
Author(s):  
Jacob Compaleo ◽  
Inder J. Gupta
Keyword(s):  
Sparse Representation ◽  
Antenna Array ◽  
Dynamic Range ◽  
Direction Of Arrival ◽  
Doa Estimation ◽  
Single Step ◽  
Window Function ◽  
Spectral Domain ◽  
Low Sidelobe ◽  
Weighting Window

Recently, we proposed a Spectral Domain Sparse Representation (SDSR) approach for the direction-of-arrival estimation of signals incident to an antenna array. In the approach, sparse representation is applied to the conventional Bartlett spectra obtained from snapshots of the signals received by the antenna array to increase the direction-of-arrival (DOA) estimation resolution and accuracy. The conventional Bartlett spectra has limited dynamic range, meaning that one may not be able to identify the presence of weak signals in the presence of strong signals. This is because, in the conventional Bartlett spectra, uniform weighting (window) is applied to signals received by various antenna elements. Apodization can be used in the generation of Bartlett spectra to increase the dynamic range of the spectra. In Apodization, more than one window function is used to generate different portions of the spectra. In this paper, we extend the SDSR approach to include Bartlett spectra obtained with Apodization and to evaluate the performance of the extended SDSR approach. We compare its performance with a two-step SDSR approach and with an approach where Bartlett spectra is obtained using a low sidelobe window function. We show that an Apodization Bartlett-based SDSR approach leads to better performance with just single-step processing.

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