An innovations approach to least squares estimation--Part IV: Recursive estimation given lumped covariance functions

We show how to recursively compute linear least squares filtered and smoothed estimates for a lumped signal process in additive white noise. However, unlike the Kalman-Bucy problem, here only the covariance function of the signal process is known and not a specific state-variable model. The solution...

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Veröffentlicht in:IEEE transactions on automatic control 1971-12, Vol.16 (6), p.720-727
Hauptverfasser: Kailath, T., Geesey, R.
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container_title IEEE transactions on automatic control
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Geesey, R.
description We show how to recursively compute linear least squares filtered and smoothed estimates for a lumped signal process in additive white noise. However, unlike the Kalman-Bucy problem, here only the covariance function of the signal process is known and not a specific state-variable model. The solutions are based on the innovations representation for the observation process.
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subjects Additive white noise
Contracts
Laboratories
Least squares approximation
Least squares methods
Nonlinear filters
Recursive estimation
Signal processing
Technological innovation
Transfer functions
title An innovations approach to least squares estimation--Part IV: Recursive estimation given lumped covariance functions
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