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Bayesian learning scheme for sparse DOA estimation based on maximum-a-posteriori of hyperparameters
Author(s) -
K. Raghu,
Neha Kumari
Publication year - 2021
Publication title -
international journal of power electronics and drive systems/international journal of electrical and computer engineering
Language(s) - English
Resource type - Journals
eISSN - 2722-2578
pISSN - 2722-256X
DOI - 10.11591/ijece.v11i4.pp3049-3058
Subject(s) - hyperparameter , maximum a posteriori estimation , bayesian inference , computer science , algorithm , bayesian probability , a priori and a posteriori , direction of arrival , bayes' theorem , signal (programming language) , inference , pattern recognition (psychology) , artificial intelligence , mathematics , antenna (radio) , statistics , maximum likelihood , telecommunications , philosophy , epistemology , programming language
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.

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