DOA Estimation for Coherent Signals With Improved Sparse Representation in the Presence of Unknown Spatially Correlated Gaussian Noise

2020 ◽  
Vol 69 (9) ◽  
pp. 10059-10069
Author(s):  
Yunfei Fang ◽  
Shengqi Zhu ◽  
Yongchan Gao ◽  
Cao Zeng
Keyword(s):  
Sparse Representation ◽  
Gaussian Noise ◽  
Doa Estimation ◽  
Coherent Signals ◽  
Spatially Correlated

DOA Estimation of Coherent Signals Using one Snapshot

2010 ◽  
Vol 32 (3) ◽  
pp. 604-608 ◽  
Author(s):  
Xin Xie ◽  
Guo-lin Li ◽  
Hua-wen Liu
Keyword(s):  
Doa Estimation ◽  
Coherent Signals

Comparison between DOA Estimation Subspace methods for incoherent and coherent signals

2015 ◽  
Vol 1 ◽  
pp. 18
Author(s):  
Ahmed Abdalla ◽  
Suhad Mohammed ◽  
Tang Bin ◽  
Jumma Mary Atieno ◽  
Abdelazeim Abdalla
Keyword(s):  
Linear Array ◽  
Doa Estimation ◽  
Far Field ◽  
Matlab Simulation ◽  
Subspace Methods ◽  
Coherent Signals ◽  
Uniform Linear Array ◽  
Pros And Cons ◽  
Basic Concepts ◽  
And Performance

This paper considers the problem of estimating the direction of arrival (DOA) for the both incoherent and coherent signals from narrowband sources, located in the far field in the case of uniform linear array sensors. Three different methods are analyzed. Specifically, these methods are Music, Root-Music and ESPRIT. The pros and cons of these methods are identified and compared in light of different viewpoints. The performance of the three methods is evaluated, analytically, when possible, and by Matlab simulation. This paper can be a roadmap for beginners in understanding the basic concepts of DOA estimation issues, properties and performance.

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.

Sign in / Sign up

Export Citation Format

Share Document