By Topic

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

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

2 Author(s)
Kailath, T. ; Stanford University, Stanford, CA, USA ; Geesey, R.

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.

Published in:

Automatic Control, IEEE Transactions on  (Volume:16 ,  Issue: 6 )