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Sparse Bayesian Learning for Long Coherent Integration Time in Passive Radar Systems

Filip, Alexandra and Shutin, Dmitriy and O'Hagan, Daniel (2017) Sparse Bayesian Learning for Long Coherent Integration Time in Passive Radar Systems. IET International Conference on Radar Systems, 23-26 October 2017, Belfast, UK.

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Abstract

Maximising the radar coherent integration time is crucial when performing detection and parameter estimation on weak target echoes. The integration time is limited however by the migration of a target of interest out of a range and Doppler cell. To account for the range migration it is proposed to build here upon a Keystone transform and develop a joint sparse super-resolution target parameter estimation and target detection method using a super-resolution sparse Bayesian learning framework. The estimation scheme uses a variational version of the space-alternating generalized expectation maximization (VB-SAGE) algorithm, which permits reducing the numerical complexity of the scheme. Moreover, since the search space is not discretized, the parameter estimates are not restricted by the system resolution. Our simulation experiments demonstrate the effectiveness of the algorithm.

Item URL in elib:https://elib.dlr.de/112862/
Document Type:Conference or Workshop Item (Speech)
Title:Sparse Bayesian Learning for Long Coherent Integration Time in Passive Radar Systems
Authors:
AuthorsInstitution or Email of AuthorsAuthors ORCID iD
Filip, Alexandraalexandra.filip (at) dlr.deUNSPECIFIED
Shutin, Dmitriydmitriy.shutin (at) dlr.deUNSPECIFIED
O'Hagan, Danieldaniel.ohagan (at) uct.ac.zaUNSPECIFIED
Date:2017
Refereed publication:Yes
Open Access:No
Gold Open Access:No
In SCOPUS:No
In ISI Web of Science:No
Status:Published
Keywords:passive radar, coherent integration time, range migration, Keystone transform, super-resolution sparse Bayesian learning
Event Title:IET International Conference on Radar Systems
Event Location:Belfast, UK
Event Type:international Conference
Event Dates:23-26 October 2017
HGF - Research field:Aeronautics, Space and Transport
HGF - Program:Aeronautics
HGF - Program Themes:air traffic management and operations
DLR - Research area:Aeronautics
DLR - Program:L AO - Air Traffic Management and Operation
DLR - Research theme (Project):L - Communication, Navigation and Surveillance
Location: Oberpfaffenhofen
Institutes and Institutions:Institute of Communication and Navigation > Communications Systems
Deposited By: Filip, Alexandra
Deposited On:01 Dec 2017 10:38
Last Modified:17 Jul 2018 14:23

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