By Topic

Locally Adaptive Cooperative Kalman Smoothing and Its Application to Identification of Nonstationary Stochastic Systems

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

1 Author(s)
Niedzwiecki, M. ; Dept. of Autom. Control, Gdansk Univ. of Technol., Gdansk, Poland

One of the central problems of the stochastic approximation theory is the proper adjustment of the smoothing algorithm to the unknown, and possibly time-varying, rate and mode of variation of the estimated signals/parameters. In this paper we propose a novel locally adaptive parallel estimation scheme which can be used to solve the problem of fixed-interval Kalman smoothing in the presence of model uncertainty. The proposed solution is based on the idea of cooperative smoothing-the Bayesian extension of the leave-one-out cross-validation approach to model selection. Within this approach the smoothed estimates are evaluated as a convex combination of the estimates provided by several competing smoothers. We derive computationally attractive algorithms allowing for cooperative Kalman smoothing and show how the proposed approach can be applied to identification of nonstationary stochastic systems.

Published in:

Signal Processing, IEEE Transactions on  (Volume:60 ,  Issue: 1 )