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Bayesian learning scheme for sparse DOA estimation based on maximum-a-posteriori of hyperparameters.
- 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]
- Subjects :
- DIRECTION of arrival estimation
RECEIVING antennas
ANTENNA arrays
Subjects
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