Artículo
Non-linear Kalman filters comparison for generalised autoregressive conditional heteroscedastic clutter parameter estimation
Fecha de publicación:
01/08/2019
Editorial:
Institution of Engineering and Technology
Revista:
Iet Signal Processing
ISSN:
1751-9675
e-ISSN:
1751-9683
Idioma:
Inglés
Tipo de recurso:
Artículo publicado
Clasificación temática:
Resumen
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.
Palabras clave:
RADAR
,
CLUTTER MODELING
,
KALMAN FILTER
,
GARCH PROCESS
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Identificadores
Colecciones
Articulos(CCT - PATAGONIA NORTE)
Articulos de CTRO.CIENTIFICO TECNOL.CONICET - PATAGONIA NORTE
Articulos de CTRO.CIENTIFICO TECNOL.CONICET - PATAGONIA NORTE
Citación
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
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