DOA Estimation From One-Bit Compressed Array Data via Joint Sparse Representation

2016 ◽  
Vol 23 (9) ◽  
pp. 1279-1283 ◽  
Author(s):  
Kai Yu ◽  
Yimin D. Zhang ◽  
Ming Bao ◽  
Yu-Hen Hu ◽  
Zhi Wang
Keyword(s):  
Sparse Representation ◽  
Doa Estimation ◽  
Array Data ◽  
Joint Sparse Representation

scholarly journals Wireless Sensor Array Network DoA Estimation from Compressed Array Data via Joint Sparse Representation

Sensors ◽  
2016 ◽  
Vol 16 (5) ◽  
pp. 686 ◽  
Author(s):  
Kai Yu ◽  
Ming Yin ◽  
Ji-An Luo ◽  
Yingguan Wang ◽  
Ming Bao ◽  
...  
Keyword(s):  
Sparse Representation ◽  
Sensor Array ◽  
Doa Estimation ◽  
Wireless Sensor ◽  
Array Data ◽  
Joint Sparse Representation

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.

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
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