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

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...

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Detalles Bibliográficos
Autores principales:	Pascual, Juan Pablo, Ellenrieder, Nicolás von, Areta, Javier A., Muravchik, Carlos Horacio
Formato:	Articulo
Lenguaje:	Inglés
Publicado:	2019
Materias:	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 GARCH process conditional variance extended Kalman filter numerical simulations radar detector
Acceso en línea:	http://sedici.unlp.edu.ar/handle/10915/125006
Aporte de:	SEDICI (UNLP) de Universidad Nacional de La Plata

id	I19-R120-10915-125006
record_format	dspace
institution	Universidad Nacional de La Plata
institution_str	I-19
repository_str	R-120
collection	SEDICI (UNLP)
language	Inglés
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
spellingShingle	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 Pascual, Juan Pablo Ellenrieder, Nicolás von Areta, Javier A. Muravchik, Carlos Horacio Non-linear Kalman filters comparison for generalised autoregressive conditional heteroscedastic clutter parameter estimation
topic_facet	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
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.
format	Articulo Articulo
author	Pascual, Juan Pablo Ellenrieder, Nicolás von Areta, Javier A. Muravchik, Carlos Horacio
author_facet	Pascual, Juan Pablo Ellenrieder, Nicolás von Areta, Javier A. Muravchik, Carlos Horacio
author_sort	Pascual, Juan Pablo
title	Non-linear Kalman filters comparison for generalised autoregressive conditional heteroscedastic clutter parameter estimation
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
publishDate	2019
url	http://sedici.unlp.edu.ar/handle/10915/125006
work_keys_str_mv	AT pascualjuanpablo nonlinearkalmanfilterscomparisonforgeneralisedautoregressiveconditionalheteroscedasticclutterparameterestimation AT ellenriedernicolasvon nonlinearkalmanfilterscomparisonforgeneralisedautoregressiveconditionalheteroscedasticclutterparameterestimation AT aretajaviera nonlinearkalmanfilterscomparisonforgeneralisedautoregressiveconditionalheteroscedasticclutterparameterestimation AT muravchikcarloshoracio nonlinearkalmanfilterscomparisonforgeneralisedautoregressiveconditionalheteroscedasticclutterparameterestimation
bdutipo_str	Repositorios
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Non-linear Kalman filters comparison for generalised autoregressive conditional heteroscedastic clutter parameter estimation

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