By Topic

Performance of adaptive estimation algorithms in dependent random environments

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
$33 $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)
R. Bitmead ; James Cook University, Queensland, Australia ; B. Anderson

We consider the convergence properties of certain algorithms arising in stochastic, discrete-time, adaptive estimation problems and operating in random environments of engineering significance. We demonstrate that the algorithms operating under ideal conditions are describable by homogeneous time-varying linear difference equations with dependent random coefficients, while in practical use, these equations are altered only through the addition of a driving term, accounting for time variation of system parameters, measurement noise, and system undermodeling. We present the concept of almost sure exponential convergence of the homogeneous difference equations as an a priori testable robustness property guaranteeing satisfactory performance in practice. For the three particular algorithms discussed, we present very mild conditions for the satisfaction of this property, and thus explain much of their observed behavior.

Published in:

IEEE Transactions on Automatic Control  (Volume:25 ,  Issue: 4 )