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Incremental Sparse Bayesian Learning for Parameter Estimation of Superimposed Signals

Shutin, Dmitriy and Wang, Wei and Jost, Thomas (2013) Incremental Sparse Bayesian Learning for Parameter Estimation of Superimposed Signals. In: 2015 International Conference on Sampling Theory and Applications, SampTA 2015. 10th International Conference on Sampling Theory and Applications, 1.-5. Jul. 2013, Bremen, Deutschland.

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Abstract

This work discuses a novel algorithm for joint sparse estimation of superimposed signals and their parameters. The proposed method is based on two concepts: a variational Bayesian version of the incremental sparse Bayesian learning (SBL)- fast variational SBL - and a variational Bayesian approach for parameter estimation of superimposed signal models. Both schemes estimate the unknown parameters by minimizing the variational lower bound on model evidence; also, these optimizations are performed incrementally with respect to the parameters of a single component. It is demonstrated that these estimations can be naturally unified under the framework of variational Bayesian inference. It allows, on the one hand, for an adaptive dictionary design for FV-SBL schemes, and, on the other hand, for a fast superresolution approach for parameter estimation of superimposed signals. The experimental evidence collected with synthetic data as well as with estimation results for measured multipath channels demonstrate the effectiveness of the proposed algorithm.

Item URL in elib:https://elib.dlr.de/82752/
Document Type:Conference or Workshop Item (Speech)
Title:Incremental Sparse Bayesian Learning for Parameter Estimation of Superimposed Signals
Authors:
AuthorsInstitution or Email of AuthorsAuthors ORCID iD
Shutin, DmitriyDmitriy.Shutin (at) dlr.deUNSPECIFIED
Wang, WeiWei.Wang (at) dlr.deUNSPECIFIED
Jost, Thomasthomas.jost (at) dlr.deUNSPECIFIED
Date:2013
Journal or Publication Title:2015 International Conference on Sampling Theory and Applications, SampTA 2015
Refereed publication:Yes
Open Access:No
Gold Open Access:No
In SCOPUS:Yes
In ISI Web of Science:No
Status:Published
Keywords:Multipath estimation, Sparse Bayesian learning
Event Title:10th International Conference on Sampling Theory and Applications
Event Location:Bremen, Deutschland
Event Type:international Conference
Event Dates:1.-5. Jul. 2013
Organizer:Jacobs University Bremen
HGF - Research field:Aeronautics, Space and Transport
HGF - Program:Aeronautics
HGF - Program Themes:ATM and Operation (old)
DLR - Research area:Aeronautics
DLR - Program:L AO - Air Traffic Management and Operation
DLR - Research theme (Project):L - Communication, Navigation and Surveillance (old)
Location: Oberpfaffenhofen
Institutes and Institutions:Institute of Communication and Navigation > Communications Systems
Deposited By: Shutin, Dmitriy
Deposited On:18 Sep 2013 09:45
Last Modified:09 Feb 2018 08:59

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