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Estimating the AR parameters from noisy observations by means of parallel Kalman filters

Abstract : The Yule Walker equations produce biased estimates of the autoregressive AR process linear prediction coeffi-cients when the observations are contaminated by an additive noise. In this paper we present an alternative se-quential approach using two Kalman filters running in parallel. Alternatively, one Kalman filter produces the AR parameters to estimate the signal while the second updates the AR parameter values from the estimated signal. Besides, the noise statistics necessary to run the Kalman filters can be estimated using the optimality properties of the Kalman filter. This method has the advantage of providing an unbiased estimation of the parameters from noisy observations and an estimation of the signal in the steady state.
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https://hal.archives-ouvertes.fr/hal-00167731
Contributor : Eric Grivel Connect in order to contact the contributor
Submitted on : Wednesday, August 22, 2007 - 2:54:30 PM
Last modification on : Thursday, January 11, 2018 - 6:21:07 AM

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  • HAL Id : hal-00167731, version 1

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Eric Grivel, David Labarre, Mohamed Najim, Ezio Todini. Estimating the AR parameters from noisy observations by means of parallel Kalman filters. COST 276, 2003, Prague, Czech Republic. pp. ⟨hal-00167731⟩

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