NDLI: Sparse Bayesian Learning with joint noise robustness and signal sparsity

Content Provider	IET Digital Library
Author	Tan, Shengbo Huang, Kaide Shang, Baolin Guo, Xuemei Wang, Guoli
Abstract	This study concerns the issue of jointly enhancing noise robustness and promoting signal sparsity in Sparse Bayesian Learning (SBL), which aims at addressing the performance deficiency of sparse signal recovery due to uninformative data with low signal-to-noise ratios. In particular, the authors propose a hierarchical prior noise model with a signal-dependent parametrisation and incorporate it into developing the robust SBL algorithms for sparse signal recovery. The main contribution of the proposed approach is twofold. The first is the new consideration of noise-robustness enhancement in building SBL algorithms, which devotes to noise awareness in counteracting outliers in measurements. Specifically, the idea of signal-sparsity enforcing is extended to build a Least Absolute Deviation like loss criterion with the proposed hierarchical prior model of measurement noise. The second is the novelty of using the signal-dependent parametrisation in the proposed noise model. Indeed, the signal-dependent mechanism plays an indispensable role in producing the reliable noise parameter estimation jointly with updating signal model parameters under the fast SBL framework. In addition to numerical simulation studies, the real-life application of radio tomographic imaging is presented to validate the proposed approach.
Starting Page	1104
Ending Page	1113
Page Count	10
ISSN	17519675
Volume Number	11
e-ISSN	17519683
Issue Number	Issue 9, Dec (2017)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-spr/11/9
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-spr.2016.0033
Journal	IET Signal Processing
Publisher Date	2017-07-21
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Bayes Method Joint Noise Robustness Jointly Enhancing Noise Robustness Learning in AI Least Absolute Deviation Measurement Noise Noise Awareness Noise-robustness Enhancement SBL Algorithm Signal Processing Signal Processing And Detection Signal Processing Theory Signal Sparsity Signal to Noise Ratio Signal-dependent Parametrisation Sparse Bayesian Learning Sparse Signal Recovery Statistics Uninformative Data
Content Type	Text
Resource Type	Article
Subject	Signal Processing Electrical and Electronic Engineering

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