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Sparse Bayesian Learning with Dictionary Refinement for Super-Resolution Through Time

Shutin, Dmitriy and Vexler, Boris (2017) Sparse Bayesian Learning with Dictionary Refinement for Super-Resolution Through Time. In: 2017 IEEE 7th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP 2017). IEEE. 2017 IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, 2017-12-10 - 2017-12-13, Curacao, Dutch Antilles. doi: 10.1109/CAMSAP.2017.8313111.

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Abstract

This work proposes an extension of a sparse Bayesian learning with dictionary refinement (SBL-DR) algorithm for a super-resolution estimation of time-varying sparse signals. Such signals are represented as a superposition of unknown but fixed number of Dirac measures with a time-varying support; as such the signal is sparse at each moment of time yet locations of Dirac measures are allowed to vary. To recover such signals an optimization framework is proposed that combines SBL-DR techniques and a penalty term that imposes smoothness constraints on the support variations in time. In contrast to state-of-the-art approaches, which typically combine parameter estimation schemes with some tracking filters, the proposed approach leads to a single objective function that permits a joint recovery of a sparse superposition of time-varying functions (trajectories). A numerical algorithm for efficient optimization of the corresponding cost function is proposed and analyzed; its performance is compared to a Kalman Enhanced Super-resolution Tracking algorithm on an example of estimating parameters of time-varying multipath channels.

Item URL in elib:https://elib.dlr.de/114411/
Document Type:Conference or Workshop Item (Poster)
Title:Sparse Bayesian Learning with Dictionary Refinement for Super-Resolution Through Time
Authors:
AuthorsInstitution or Email of AuthorsAuthor's ORCID iDORCID Put Code
Shutin, DmitriyUNSPECIFIEDhttps://orcid.org/0000-0002-6065-6453UNSPECIFIED
Vexler, BorisUNSPECIFIEDUNSPECIFIEDUNSPECIFIED
Date:10 December 2017
Journal or Publication Title:2017 IEEE 7th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP 2017)
Refereed publication:Yes
Open Access:No
Gold Open Access:No
In SCOPUS:No
In ISI Web of Science:No
DOI:10.1109/CAMSAP.2017.8313111
Publisher:IEEE
Status:Published
Keywords:Sparse signal reconstruction, time-varying signals, sparse Bayesian learning, super-resolution
Event Title:2017 IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing
Event Location:Curacao, Dutch Antilles
Event Type:Workshop
Event Start Date:10 December 2017
Event End Date:13 December 2017
Organizer:IEEE Signal Processing Socienty
HGF - Research field:Aeronautics, Space and Transport
HGF - Program:Space
HGF - Program Themes:Communication and Navigation
DLR - Research area:Raumfahrt
DLR - Program:R KN - Kommunikation und Navigation
DLR - Research theme (Project):R - Project Navigation 4.0 (old)
Location: Oberpfaffenhofen
Institutes and Institutions:Institute of Communication and Navigation > Communications Systems
Deposited By: Shutin, Dmitriy
Deposited On:08 Feb 2018 14:42
Last Modified:24 Apr 2024 20:18

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