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

dc.creatorPascual, Juan Pablo
dc.creatorEllenrieder, Nicolás von
dc.creatorAreta, Javier A.
dc.creatorMuravchik, Carlos Horacio
dc.date2019-08
dc.date2021-09-16T17:33:00Z
dc.date.accessioned2023-07-15T03:16:38Z
dc.date.available2023-07-15T03:16:38Z
dc.identifierhttp://sedici.unlp.edu.ar/handle/10915/125006
dc.identifierissn:1751-9675
dc.identifierissn:1751-9683
dc.identifier.urihttps://repositorioslatinoamericanos.uchile.cl/handle/2250/7465100
dc.descriptionIn 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.
dc.descriptionFacultad de Ingeniería
dc.descriptionInstituto de Investigaciones en Electrónica, Control y Procesamiento de Señales
dc.formatapplication/pdf
dc.format606-613
dc.languageen
dc.rightshttp://creativecommons.org/licenses/by-nc-sa/4.0/
dc.rightsCreative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)
dc.subjectIngeniería
dc.subjectIngeniería Electrónica
dc.subjectradar clutter
dc.subjectautoregressive processes
dc.subjectradar detection
dc.subjectKalman filters
dc.subjectnonlinear filters
dc.subjectparameter estimation
dc.subjectnonlinear Kalman filters
dc.subjectGARCH process coefficients
dc.subjectunscented Kalman filter
dc.subjectcubature Kalman filter
dc.subjectsecond-order nonlinear terms
dc.subjectgeneralised autoregressive conditional heteroscedastic clutter
dc.subjectparameter estimation
dc.subjectGARCH process conditional variance
dc.subjectextended Kalman filter
dc.subjectnumerical simulations
dc.subjectradar detector
dc.titleNon-linear Kalman filters comparison for generalised autoregressive conditional heteroscedastic clutter parameter estimation
dc.typeArticulo
dc.typeArticulo


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