Academic Journal
Sparse Estimation Using Bayesian Hierarchical Prior Modeling for Real and Complex Linear Models
| العنوان: | Sparse Estimation Using Bayesian Hierarchical Prior Modeling for Real and Complex Linear Models |
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| المؤلفون: | Pedersen, Niels Lovmand, Manchón, Carles Navarro, Badiu, Mihai Alin, Shutin, Dmitriy, Fleury, Bernard Henri |
| المصدر: | Pedersen , N L , Manchón , C N , Badiu , M A , Shutin , D & Fleury , B H 2015 , ' Sparse Estimation Using Bayesian Hierarchical Prior Modeling for Real and Complex Linear Models ' , Signal Processing , vol. 115 , pp. 94-109 . https://doi.org/10.1016/j.sigpro.2015.03.013 |
| سنة النشر: | 2015 |
| المجموعة: | Aalborg University (AAU): Publications / Aalborg Universitet: Publikationer |
| مصطلحات موضوعية: | Sparse Bayesian learning, Sparse signal representations, Underdetermined linear systems, Hierarchical Bayesian Modeling, Sparsity-inducing priors |
| الوصف: | In sparse Bayesian learning (SBL), Gaussian scale mixtures (GSMs) have been used to model sparsity-inducing priors that realize a class of concave penalty functions for the regression task in real-valued signal models. Motivated by the relative scarcity of formal tools for SBL in complex-valued models, this paper proposes a GSM model - the Bessel K model - that induces concave penalty functions for the estimation of complex sparse signals. The properties of the Bessel K model are analyzed when it is applied to Type I and Type II estimation. This analysis reveals that, by tuning the parameters of the mixing pdf different penalty functions are invoked depending on the estimation type used, the value of the noise variance, and whether real or complex signals are estimated. Using the Bessel K model, we derive a sparse estimator based on a modification of the expectation-maximization algorithm formulated for Type II estimation. The estimator includes as a special instance the algorithms proposed by Tipping and Faul [1] and by Babacan et al. [2]. Numerical results show the superiority of the proposed estimator over these state-of-the-art estimators in terms of convergence speed, sparseness, reconstruction error, and robustness in low and medium signal-to-noise ratio regimes. |
| نوع الوثيقة: | article in journal/newspaper |
| وصف الملف: | multipart/x-zip |
| اللغة: | English |
| DOI: | 10.1016/j.sigpro.2015.03.013 |
| الاتاحة: | https://vbn.aau.dk/da/publications/c8a1ba38-bbc0-488e-9d29-bd636c016ef5 https://doi.org/10.1016/j.sigpro.2015.03.013 https://vbn.aau.dk/ws/files/205913102/fast_besselk.zip |
| Rights: | info:eu-repo/semantics/openAccess |
| رقم الانضمام: | edsbas.3FF29BA |
| قاعدة البيانات: | BASE |
| DOI: | 10.1016/j.sigpro.2015.03.013 |
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