scholarly journals Sparse representation based direction‐of‐arrival estimation in nonuniform noise via tail minimisation

2021 ◽  
Author(s):  
Cao Zeng ◽  
Yunfei Fang ◽  
Jun Li ◽  
Minti Liu ◽  
Shidong Li
Keyword(s):  
Sparse Representation ◽  
Direction Of Arrival ◽  
Direction Of Arrival Estimation

Direction of arrival estimation of near‐field sources using sparse representation framework.

2011 ◽  
Vol 129 (4) ◽  
pp. 2664-2664
Author(s):  
Yinghui Zhao ◽  
Mahmood R. Azimi‐Sadjadi ◽  
Amanda Dinstel
Keyword(s):  
Sparse Representation ◽  
Near Field ◽  
Direction Of Arrival ◽  
Direction Of Arrival Estimation

scholarly journals Covariance-Based Direction-of-Arrival Estimation of Wideband Coherent Chirp Signals via Sparse Representation

Sensors ◽  
2013 ◽  
Vol 13 (9) ◽  
pp. 11490-11497 ◽  
Author(s):  
Zhichao Sha ◽  
Zhengmeng Liu ◽  
Zhitao Huang ◽  
Yiyu Zhou
Keyword(s):  
Sparse Representation ◽  
Direction Of Arrival ◽  
Direction Of Arrival Estimation ◽  
Chirp Signals

scholarly journals Noncircular Sources-Based Sparse Representation Algorithm for Direction of Arrival Estimation in MIMO Radar with Mutual Coupling

Algorithms ◽  
2016 ◽  
Vol 9 (3) ◽  
pp. 61 ◽  
Author(s):  
Weidong Zhou ◽  
Jing Liu ◽  
Pengxiang Zhu ◽  
Wenhe Gong ◽  
Jiaxin Hou
Keyword(s):  
Sparse Representation ◽  
Mutual Coupling ◽  
Direction Of Arrival ◽  
Mimo Radar ◽  
Direction Of Arrival Estimation

scholarly journals Application of Sparse Representation to Bartlett Spectra for Improved Direction of Arrival Estimation

Sensors ◽  
2020 ◽  
Vol 21 (1) ◽  
pp. 77
Author(s):  
Jacob Compaleo ◽  
Inder J. Gupta
Keyword(s):  
Sparse Representation ◽  
Regularization Parameter ◽  
Angular Separation ◽  
Direction Of Arrival ◽  
Doa Estimation ◽  
Direction Of Arrival Estimation ◽  
Small Signal ◽  
Music Algorithm ◽  
A New Technique ◽  
Better Than

A new technique for high-resolution direction of arrival estimation is presented. The method utilizes the traditional Bartlett spectra and sparse representation to locate emitters in single and multiple emitter scenarios. A method for selecting the sparse representation regularization parameter is also presented. Using Monte Carlo simulations, we show that the proposed approach achieves accurate direction of arrival (DOA) estimations that are unbiased and a variance that approaches the Cramer–Rao lower bound. We show that our method outperforms the popular MUSIC algorithm, and is slightly better than the sparse representation based L1-SVD algorithm when angular separation between emitters is small, signal SNR is low, and a small number of snapshots are used in DOA estimation.

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