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Bayesian learning scheme for sparse DOA estimation based on maximum-a-posteriori of hyperparameters.

Authors :
K., Raghu
N., Prameela Kumari
Source :
International Journal of Electrical & Computer Engineering (2088-8708); Aug2021, Vol. 11 Issue 4, p3049-3058, 10p
Publication Year :
2021

Abstract

In this paper, the problem of direction of arrival estimation is addressed by employing Bayesian learning technique in sparse domain. This paper deals with the inference of sparse Bayesian learning (SBL) for both single measurement vector (SMV) and multiple measurement vector (MMV) and its applicability to estimate the arriving signal's direction at the receiving antenna array; particularly considered to be a uniform linear array. We also derive the hyperparameter updating equations by maximizing the posterior of hyperparameters and exhibit the results for nonzero hyperprior scalars. The results presented in this paper, shows that the resolution and speed of the proposed algorithm is comparatively improved with almost zero failure rate and minimum mean square error of signal's direction estimate. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
20888708
Volume :
11
Issue :
4
Database :
Complementary Index
Journal :
International Journal of Electrical & Computer Engineering (2088-8708)
Publication Type :
Academic Journal
Accession number :
149982486
Full Text :
https://doi.org/10.11591/ijece.v11i4.pp3049-3058