Cramér–Rao Bounds for DoA Estimation of Sparse Bayesian Learning with the Laplace Prior

In this paper, we derive the Cramér–Rao lower bounds (CRLB) for direction of arrival (DoA) estimation by using sparse Bayesian learning (SBL) and the Laplace prior. CRLB is a lower bound on the variance of the estimator, the change of CRLB can indicate the effect of the specific factor to the DoA es...

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Detalles Bibliográficos
Autores principales: Bai, Hua, Duarte, Marco F., Janaswamy, Ramakrishna
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Communication
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9824496/
https://www.ncbi.nlm.nih.gov/pubmed/36616904
http://dx.doi.org/10.3390/s23010307

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9824496/
https://www.ncbi.nlm.nih.gov/pubmed/36616904
http://dx.doi.org/10.3390/s23010307

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