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

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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Bibliographic Details
Main Authors: Hua Bai, Marco F. Duarte, Ramakrishna Janaswamy
Format: Article
Language:English
Published: MDPI AG 2022-12-01
Series:Sensors
Subjects:
Cramér–Rao lower bound
sparse Bayesian learning
Bayesian CRLB
Laplace prior
nonuniform array
Online Access:https://www.mdpi.com/1424-8220/23/1/307

Internet

https://www.mdpi.com/1424-8220/23/1/307

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