Heterogeneous Bayesian compressive sensing for sparse signal recovery
- Author(s): Kaide Huang 1 ; Yao Guo 1 ; Xuemei Guo 1 ; Guoli Wang 1
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View affiliations
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Affiliations:
1:
School of Information Science and Technology, Sun Yat-Sen University, Guangzhou 510006, People's Republic of China
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Affiliations:
1:
School of Information Science and Technology, Sun Yat-Sen University, Guangzhou 510006, People's Republic of China
- Source:
Volume 8, Issue 9,
December 2014,
p.
1009 – 1017
DOI: 10.1049/iet-spr.2013.0501 , Print ISSN 1751-9675, Online ISSN 1751-9683
© The Institution of Engineering and Technology
Received
26/12/2013,
Accepted
27/06/2014,
Revised
31/05/2014,
Published
16/12/2014
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.
Inspec keywords: learning (artificial intelligence); signal denoising; belief networks; Bayes methods; compressed sensing
Other keywords: heterogeneous Bayesian compressive sensing; sparse Bayesian learning; sparse signal recovery; noise variance learning; performance improvement; homogeneous noise; applicability enhancement; heterogeneous noise model
Subjects: Signal processing and detection; Other topics in statistics; Learning in AI (theory); Signal processing theory
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