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
- Institución
- Universidad Nacional de La Plata
- OAI Identificador
- oai:sedici.unlp.edu.ar:10915/125006
Ver los metadatos del registro completo
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 |
_version_ |
1842260518153224192 |
score |
13.13397 |