Non-linear Kalman filters comparison for generalised autoregressive conditional heteroscedastic clutter parameter estimation

Autores
Pascual, Juan Pablo; Von Ellenrieder, Nicolás; Areta, Javier Alberto; Muravchik, Carlos Horacio
Año de publicación
2019
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
In this work, the authors analyse the estimation of the generalised autoregressive conditional heteroscedastic (GARCH) process conditional variance based on three non-linear filtering approaches: extended Kalman filter (EKF), unscented Kalman filter and cubature Kalman filter. The authors present a state model for a GARCH process and derive an EKF including second-order non-linear terms for simultaneous estimation of state and parameters. Using synthetic data, the authors evaluate the consistency and the correlation of the innovations for the three filters, by means of numerical simulations. The authors also study the performance of smoothed versions of the non-linear Kalman filters using real clutter data in comparison with a conventional quasi-maximum likelihood estimation method for the GARCH process coefficients. The authors show that with all methods the process coefficients estimates are of the same order and the resulting conditional variances are commensurable. However, the non-linear Kalman filters greatly reduce the computational load. These kind of filters could be used for the radar detector based on a GARCH clutter model that uses an adaptive threshold that demands the conditional variance at each decision instant.
Fil: Pascual, Juan Pablo. Comisión Nacional de Energía Atómica. Gerencia del Área de Energía Nuclear. Instituto Balseiro; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Patagonia Norte; Argentina
Fil: Von Ellenrieder, Nicolás. McGill University; Canadá
Fil: Areta, Javier Alberto. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Patagonia Norte; Argentina. Universidad Nacional de Río Negro; Argentina
Fil: Muravchik, Carlos Horacio. Provincia de Buenos Aires. Gobernación. Comisión de Investigaciones Científicas; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Instituto de Investigaciones en Electrónica, Control y Procesamiento de Señales. Universidad Nacional de La Plata. Instituto de Investigaciones en Electrónica, Control y Procesamiento de Señales; Argentina
Materia
RADAR
CLUTTER MODELING
KALMAN FILTER
GARCH PROCESS
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by-nc-nd/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/124866
Acceder
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network_acronym_str CONICETDig
repository_id_str 3498
network_name_str CONICET Digital (CONICET)
spelling Non-linear Kalman filters comparison for generalised autoregressive conditional heteroscedastic clutter parameter estimationPascual, Juan PabloVon Ellenrieder, NicolásAreta, Javier AlbertoMuravchik, Carlos HoracioRADARCLUTTER MODELINGKALMAN FILTERGARCH PROCESShttps://purl.org/becyt/ford/2.2https://purl.org/becyt/ford/2In this work, the authors analyse the estimation of the generalised autoregressive conditional heteroscedastic (GARCH) process conditional variance based on three non-linear filtering approaches: extended Kalman filter (EKF), unscented Kalman filter and cubature Kalman filter. The authors present a state model for a GARCH process and derive an EKF including second-order non-linear terms for simultaneous estimation of state and parameters. Using synthetic data, the authors evaluate the consistency and the correlation of the innovations for the three filters, by means of numerical simulations. The authors also study the performance of smoothed versions of the non-linear Kalman filters using real clutter data in comparison with a conventional quasi-maximum likelihood estimation method for the GARCH process coefficients. The authors show that with all methods the process coefficients estimates are of the same order and the resulting conditional variances are commensurable. However, the non-linear Kalman filters greatly reduce the computational load. These kind of filters could be used for the radar detector based on a GARCH clutter model that uses an adaptive threshold that demands the conditional variance at each decision instant.Fil: Pascual, Juan Pablo. Comisión Nacional de Energía Atómica. Gerencia del Área de Energía Nuclear. Instituto Balseiro; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Patagonia Norte; ArgentinaFil: Von Ellenrieder, Nicolás. McGill University; CanadáFil: Areta, Javier Alberto. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Patagonia Norte; Argentina. Universidad Nacional de Río Negro; ArgentinaFil: Muravchik, Carlos Horacio. Provincia de Buenos Aires. Gobernación. Comisión de Investigaciones Científicas; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Instituto de Investigaciones en Electrónica, Control y Procesamiento de Señales. Universidad Nacional de La Plata. Instituto de Investigaciones en Electrónica, Control y Procesamiento de Señales; ArgentinaInstitution of Engineering and Technology2019-08-01info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/124866Pascual, Juan Pablo; Von Ellenrieder, Nicolás; Areta, Javier Alberto; Muravchik, Carlos Horacio; Non-linear Kalman filters comparison for generalised autoregressive conditional heteroscedastic clutter parameter estimation; Institution of Engineering and Technology; Iet Signal Processing; 13; 6; 1-8-2019; 606-6131751-96751751-9683CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/url/https://digital-library.theiet.org/content/journals/10.1049/iet-spr.2018.5400info:eu-repo/semantics/altIdentifier/doi/10.1049/iet-spr.2018.5400info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-nd/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-03T09:44:49Zoai:ri.conicet.gov.ar:11336/124866instacron:CONICETInstitucionalhttp://ri.conicet.gov.ar/Organismo científico-tecnológicoNo correspondehttp://ri.conicet.gov.ar/oai/requestdasensio@conicet.gov.ar; lcarlino@conicet.gov.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:34982025-09-03 09:44:49.651CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Non-linear Kalman filters comparison for generalised autoregressive conditional heteroscedastic clutter parameter estimation
title Non-linear Kalman filters comparison for generalised autoregressive conditional heteroscedastic clutter parameter estimation
spellingShingle Non-linear Kalman filters comparison for generalised autoregressive conditional heteroscedastic clutter parameter estimation
Pascual, Juan Pablo
RADAR
CLUTTER MODELING
KALMAN FILTER
GARCH PROCESS
title_short Non-linear Kalman filters comparison for generalised autoregressive conditional heteroscedastic clutter parameter estimation
title_full Non-linear Kalman filters comparison for generalised autoregressive conditional heteroscedastic clutter parameter estimation
title_fullStr Non-linear Kalman filters comparison for generalised autoregressive conditional heteroscedastic clutter parameter estimation
title_full_unstemmed Non-linear Kalman filters comparison for generalised autoregressive conditional heteroscedastic clutter parameter estimation
title_sort Non-linear Kalman filters comparison for generalised autoregressive conditional heteroscedastic clutter parameter estimation
dc.creator.none.fl_str_mv Pascual, Juan Pablo
Von Ellenrieder, Nicolás
Areta, Javier Alberto
Muravchik, Carlos Horacio
author Pascual, Juan Pablo
author_facet Pascual, Juan Pablo
Von Ellenrieder, Nicolás
Areta, Javier Alberto
Muravchik, Carlos Horacio
author_role author
author2 Von Ellenrieder, Nicolás
Areta, Javier Alberto
Muravchik, Carlos Horacio
author2_role author
author
author
dc.subject.none.fl_str_mv RADAR
CLUTTER MODELING
KALMAN FILTER
GARCH PROCESS
topic RADAR
CLUTTER MODELING
KALMAN FILTER
GARCH PROCESS
purl_subject.fl_str_mv https://purl.org/becyt/ford/2.2
https://purl.org/becyt/ford/2
dc.description.none.fl_txt_mv In this work, the authors analyse the estimation of the generalised autoregressive conditional heteroscedastic (GARCH) process conditional variance based on three non-linear filtering approaches: extended Kalman filter (EKF), unscented Kalman filter and cubature Kalman filter. The authors present a state model for a GARCH process and derive an EKF including second-order non-linear terms for simultaneous estimation of state and parameters. Using synthetic data, the authors evaluate the consistency and the correlation of the innovations for the three filters, by means of numerical simulations. The authors also study the performance of smoothed versions of the non-linear Kalman filters using real clutter data in comparison with a conventional quasi-maximum likelihood estimation method for the GARCH process coefficients. The authors show that with all methods the process coefficients estimates are of the same order and the resulting conditional variances are commensurable. However, the non-linear Kalman filters greatly reduce the computational load. These kind of filters could be used for the radar detector based on a GARCH clutter model that uses an adaptive threshold that demands the conditional variance at each decision instant.
Fil: Pascual, Juan Pablo. Comisión Nacional de Energía Atómica. Gerencia del Área de Energía Nuclear. Instituto Balseiro; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Patagonia Norte; Argentina
Fil: Von Ellenrieder, Nicolás. McGill University; Canadá
Fil: Areta, Javier Alberto. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Patagonia Norte; Argentina. Universidad Nacional de Río Negro; Argentina
Fil: Muravchik, Carlos Horacio. Provincia de Buenos Aires. Gobernación. Comisión de Investigaciones Científicas; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Instituto de Investigaciones en Electrónica, Control y Procesamiento de Señales. Universidad Nacional de La Plata. Instituto de Investigaciones en Electrónica, Control y Procesamiento de Señales; Argentina
description In this work, the authors analyse the estimation of the generalised autoregressive conditional heteroscedastic (GARCH) process conditional variance based on three non-linear filtering approaches: extended Kalman filter (EKF), unscented Kalman filter and cubature Kalman filter. The authors present a state model for a GARCH process and derive an EKF including second-order non-linear terms for simultaneous estimation of state and parameters. Using synthetic data, the authors evaluate the consistency and the correlation of the innovations for the three filters, by means of numerical simulations. The authors also study the performance of smoothed versions of the non-linear Kalman filters using real clutter data in comparison with a conventional quasi-maximum likelihood estimation method for the GARCH process coefficients. The authors show that with all methods the process coefficients estimates are of the same order and the resulting conditional variances are commensurable. However, the non-linear Kalman filters greatly reduce the computational load. These kind of filters could be used for the radar detector based on a GARCH clutter model that uses an adaptive threshold that demands the conditional variance at each decision instant.
publishDate 2019
dc.date.none.fl_str_mv 2019-08-01
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
http://purl.org/coar/resource_type/c_6501
info:ar-repo/semantics/articulo
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/11336/124866
Pascual, Juan Pablo; Von Ellenrieder, Nicolás; Areta, Javier Alberto; Muravchik, Carlos Horacio; Non-linear Kalman filters comparison for generalised autoregressive conditional heteroscedastic clutter parameter estimation; Institution of Engineering and Technology; Iet Signal Processing; 13; 6; 1-8-2019; 606-613
1751-9675
1751-9683
CONICET Digital
CONICET
url http://hdl.handle.net/11336/124866
identifier_str_mv Pascual, Juan Pablo; Von Ellenrieder, Nicolás; Areta, Javier Alberto; Muravchik, Carlos Horacio; Non-linear Kalman filters comparison for generalised autoregressive conditional heteroscedastic clutter parameter estimation; Institution of Engineering and Technology; Iet Signal Processing; 13; 6; 1-8-2019; 606-613
1751-9675
1751-9683
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/url/https://digital-library.theiet.org/content/journals/10.1049/iet-spr.2018.5400
info:eu-repo/semantics/altIdentifier/doi/10.1049/iet-spr.2018.5400
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by-nc-nd/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by-nc-nd/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv Institution of Engineering and Technology
publisher.none.fl_str_mv Institution of Engineering and Technology
dc.source.none.fl_str_mv reponame:CONICET Digital (CONICET)
instname:Consejo Nacional de Investigaciones Científicas y Técnicas
reponame_str CONICET Digital (CONICET)
collection CONICET Digital (CONICET)
instname_str Consejo Nacional de Investigaciones Científicas y Técnicas
repository.name.fl_str_mv CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicas
repository.mail.fl_str_mv dasensio@conicet.gov.ar; lcarlino@conicet.gov.ar
_version_ 1842268691012517888
score 13.13397

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