Off-Grid Sparse Bayesian Learning Algorithm for Compressed Sparse Array

The compressed sparse array (CSA) structure combines the advantages of the sparse array and compressed sensing, which not only reduces the number of antennas, but also decreases the number of RF front-end channels, thus obtaining high estimation performance with low complexity. However, the existing...

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Bibliographic Details
Published in2021 CIE International Conference on Radar (Radar) pp. 2308 - 2312
Main Authors Guo, Limin, Xiao, Siqi, Guo, Muran
Format Conference Proceeding
LanguageEnglish
Published IEEE 15.12.2021
Subjects
Bayes methods
Direction-of-arrival estimation
DOA estimation
Estimation
Focusing
off-grid
Radar
Radio frequency
Simulation
sparse array
sparse Bayesian learning
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Summary:The compressed sparse array (CSA) structure combines the advantages of the sparse array and compressed sensing, which not only reduces the number of antennas, but also decreases the number of RF front-end channels, thus obtaining high estimation performance with low complexity. However, the existing under-determined direction of arrival (DOA) estimation algorithms for CSA are based on compression reconstruction so far, where the accuracy and resolution of estimation are limited by the off-grid effect. Focusing on this problem, this paper proposes an estimation algorithm named CSA-based off-grid sparse Bayesian learning (CSA-OGSBL). The proposed CSA-OGSBL algorithm presents a system model in which we consider the unknown variance into the signal vector of interest and improves the traditional off-grid method through updating the grid points iteratively. The superiority of the proposed CSA-OGSBL algorithm in terms of estimation precision is identified by simulation results.
ISSN:2640-7736
DOI:10.1109/Radar53847.2021.10028151