Ecole d'ingénieur et centre de recherche en Sciences du numérique

Sparse recovery using an iterative variational Bayes algorithm and application to AoA estimation

Bazzi, Ahmad; Slock, Dirk T.M; Meilhac, Lisa

ISSPIT 2016, 16th International Symposium on Signal Processing and Information Technology, 12-14 December 2016, Limassol, Cyprus

This paper presents an iterative Variational Bayes (VB) algorithm that allows sparse recovery of the desired transmitted vector. The VB algorithm is derived based on the latent variables introduced in the Bayesian model in hand. The proposed algorithm is applied to he Angle-of-Arrival (AoA) estimation problem and simulations demonstrate the potential of the proposed VB algorithm when compared to existing sparse recovery and compressed sensing algorithms, especially in the case of closely spaced sources. Furthermore, the proposed algorithm does not require prior knowledge of the number of sources and operates with only one snapshot.

Document Doi Bibtex

Titre:Sparse recovery using an iterative variational Bayes algorithm and application to AoA estimation
Mots Clés:Sparse Recovery, Variational Bayes, Iterative, Latent Variables, Angle-of-Arrival
Département:Systèmes de Communication
Eurecom ref:5127
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Bibtex: @inproceedings{EURECOM+5127, doi = {}, year = {2016}, title = {{S}parse recovery using an iterative variational {B}ayes algorithm and application to {A}o{A} estimation}, author = {{B}azzi, {A}hmad and {S}lock, {D}irk {T}.{M} and {M}eilhac, {L}isa}, booktitle = {{ISSPIT} 2016, 16th {I}nternational {S}ymposium on {S}ignal {P}rocessing and {I}nformation {T}echnology, 12-14 {D}ecember 2016, {L}imassol, {C}yprus }, address = {{L}imassol, {CHYPRE}}, month = {12}, url = {} }
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