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

Autores
Pascual, Juan Pablo; Ellenrieder, Nicolás von; Areta, Javier A.; 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.
Facultad de Ingeniería
Instituto de Investigaciones en Electrónica, Control y Procesamiento de Señales
Materia
Ingeniería
Ingeniería Electrónica
radar clutter
autoregressive processes
radar detection
Kalman filters
nonlinear filters
parameter estimation
nonlinear Kalman filters
GARCH process coefficients
unscented Kalman filter
cubature Kalman filter
second-order nonlinear terms
generalised autoregressive conditional heteroscedastic clutter
parameter estimation
GARCH process conditional variance
extended Kalman filter
numerical simulations
radar detector
Nivel de accesibilidad
acceso abierto
Condiciones de uso
http://creativecommons.org/licenses/by-nc-sa/4.0/
Repositorio
SEDICI (UNLP)
Institución
Universidad Nacional de La Plata
OAI Identificador
oai:sedici.unlp.edu.ar:10915/125006

id SEDICI_88f514f6c99e020f0bbc9827dff8b249
oai_identifier_str oai:sedici.unlp.edu.ar:10915/125006
network_acronym_str SEDICI
repository_id_str 1329
network_name_str SEDICI (UNLP)
spelling Non-linear Kalman filters comparison for generalised autoregressive conditional heteroscedastic clutter parameter estimationPascual, Juan PabloEllenrieder, Nicolás vonAreta, Javier A.Muravchik, Carlos HoracioIngenieríaIngeniería Electrónicaradar clutterautoregressive processesradar detectionKalman filtersnonlinear filtersparameter estimationnonlinear Kalman filtersGARCH process coefficientsunscented Kalman filtercubature Kalman filtersecond-order nonlinear termsgeneralised autoregressive conditional heteroscedastic clutterparameter estimationGARCH process conditional varianceextended Kalman filternumerical simulationsradar detectorIn 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.Facultad de IngenieríaInstituto de Investigaciones en Electrónica, Control y Procesamiento de Señales2019-08info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionArticulohttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdf606-613http://sedici.unlp.edu.ar/handle/10915/125006enginfo:eu-repo/semantics/altIdentifier/issn/1751-9675info:eu-repo/semantics/altIdentifier/issn/1751-9683info:eu-repo/semantics/altIdentifier/doi/10.1049/iet-spr.2018.5400info:eu-repo/semantics/openAccesshttp://creativecommons.org/licenses/by-nc-sa/4.0/Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)reponame:SEDICI (UNLP)instname:Universidad Nacional de La Platainstacron:UNLP2025-09-03T11:01:59Zoai:sedici.unlp.edu.ar:10915/125006Institucionalhttp://sedici.unlp.edu.ar/Universidad públicaNo correspondehttp://sedici.unlp.edu.ar/oai/snrdalira@sedici.unlp.edu.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:13292025-09-03 11:01:59.918SEDICI (UNLP) - Universidad Nacional de La Platafalse
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
Ingeniería
Ingeniería Electrónica
radar clutter
autoregressive processes
radar detection
Kalman filters
nonlinear filters
parameter estimation
nonlinear Kalman filters
GARCH process coefficients
unscented Kalman filter
cubature Kalman filter
second-order nonlinear terms
generalised autoregressive conditional heteroscedastic clutter
parameter estimation
GARCH process conditional variance
extended Kalman filter
numerical simulations
radar detector
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
Ellenrieder, Nicolás von
Areta, Javier A.
Muravchik, Carlos Horacio
author Pascual, Juan Pablo
author_facet Pascual, Juan Pablo
Ellenrieder, Nicolás von
Areta, Javier A.
Muravchik, Carlos Horacio
author_role author
author2 Ellenrieder, Nicolás von
Areta, Javier A.
Muravchik, Carlos Horacio
author2_role author
author
author
dc.subject.none.fl_str_mv Ingeniería
Ingeniería Electrónica
radar clutter
autoregressive processes
radar detection
Kalman filters
nonlinear filters
parameter estimation
nonlinear Kalman filters
GARCH process coefficients
unscented Kalman filter
cubature Kalman filter
second-order nonlinear terms
generalised autoregressive conditional heteroscedastic clutter
parameter estimation
GARCH process conditional variance
extended Kalman filter
numerical simulations
radar detector
topic Ingeniería
Ingeniería Electrónica
radar clutter
autoregressive processes
radar detection
Kalman filters
nonlinear filters
parameter estimation
nonlinear Kalman filters
GARCH process coefficients
unscented Kalman filter
cubature Kalman filter
second-order nonlinear terms
generalised autoregressive conditional heteroscedastic clutter
parameter estimation
GARCH process conditional variance
extended Kalman filter
numerical simulations
radar detector
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.
Facultad de Ingeniería
Instituto de Investigaciones en Electrónica, Control y Procesamiento de Señales
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
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
Articulo
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://sedici.unlp.edu.ar/handle/10915/125006
url http://sedici.unlp.edu.ar/handle/10915/125006
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/issn/1751-9675
info:eu-repo/semantics/altIdentifier/issn/1751-9683
info:eu-repo/semantics/altIdentifier/doi/10.1049/iet-spr.2018.5400
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
http://creativecommons.org/licenses/by-nc-sa/4.0/
Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)
eu_rights_str_mv openAccess
rights_invalid_str_mv http://creativecommons.org/licenses/by-nc-sa/4.0/
Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)
dc.format.none.fl_str_mv application/pdf
606-613
dc.source.none.fl_str_mv reponame:SEDICI (UNLP)
instname:Universidad Nacional de La Plata
instacron:UNLP
reponame_str SEDICI (UNLP)
collection SEDICI (UNLP)
instname_str Universidad Nacional de La Plata
instacron_str UNLP
institution UNLP
repository.name.fl_str_mv SEDICI (UNLP) - Universidad Nacional de La Plata
repository.mail.fl_str_mv alira@sedici.unlp.edu.ar
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