Conditional variance LMMSE estimator for a GARCH process clutter model

Conditional variance LMMSE estimator for a GARCH process clutter model

A radar detection scheme based on a GARCH clutter model has been proposed recently. This adaptive detector depends on the conditional variance of the GARCH process. We present a linear minimum mean square error (LMMSE) estimator for the conditional variance of a GARCH process that allows us to updat...

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Bibliographic Details
Published in:2014 IEEE 8th Sensor Array and Multichannel Signal Processing Workshop (SAM) pp. 309 - 312
Main Authors: Pascual, Juan Pablo, von Ellenrieder, Nicolas, Muravchik, Carlos H.
Format: Conference Proceeding
Language:English
Published: IEEE 01-06-2014
Subjects:
Clutter
Equations
Estimation
Kalman filters
Mathematical model
Vectors
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Summary:A radar detection scheme based on a GARCH clutter model has been proposed recently. This adaptive detector depends on the conditional variance of the GARCH process. We present a linear minimum mean square error (LMMSE) estimator for the conditional variance of a GARCH process that allows us to update the conditional variance at each decision instant. We derive the estimation algorithm with an approach analogous to the Kalman filter, though system matrices turn out to be random ones. We illustrate the LMMSE algorithm behavior by means of simulations with a particular GARCH process.
ISSN:1551-2282
2151-870X
DOI:10.1109/SAM.2014.6882403