Open AccessHeterogeneous Bayesian compressive sensing for sparse signal recovery
Author(s) -
Huang Kaide,
Guo Yao,
Guo Xuemei,
Wang Guoli
Publication year - 2014
Publication title -
iet signal processing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.384
H-Index - 42
eISSN - 1751-9683
pISSN - 1751-9675
DOI - 10.1049/iet-spr.2013.0501
Subject(s) - compressed sensing , bayesian probability , computer science , noise (video) , context (archaeology) , signal (programming language) , signal reconstruction , variance (accounting) , artificial intelligence , bayesian inference , machine learning , pattern recognition (psychology) , signal processing , image (mathematics) , telecommunications , paleontology , radar , accounting , business , biology , programming language
This study focuses on the issue of sparse signal recovery with sparse Bayesian learning in the context of a heterogeneous noise model, called by the heterogeneous Bayesian compressive sensing. The main contribution is to exploit the capability of noise variance learning in performance improvement and applicability enhancement. Experimental results on synthetic and real‐world data demonstrate that heterogeneous Bayesian compressive sensing has superior performance in terms of accuracy and sparsity for both homogeneous and heterogeneous noise scenarios.