By Topic

Stable recursive estimators for systems with multiple models

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)
Nagpal, K.M. ; Stand. & Poor''s, New York, NY, USA

In many practical situations, such as target tracking of manoeuvring objects and fault detection, one is faced with the task of estimating the state of the system when the model describing the evolution of the system changes abruptly. In this paper we develop an approach for estimating state for systems where the model at any given time is from a finite set of models, though one does not know which model is applicable at any given time. We provide sufficient conditions that guarantee the stability of the filter and at the same time provide a measure of performance of the filter compared to the case when the model is known. The algorithm is easy to implement and does not suffer from combinatoric complexity of considering all trajectories of models. The algorithm involves a set of parallel Kalman filters (one for each possible model) and a linear matrix inequality (LMI) based “mixing” of estimates at every stage

Published in:

American Control Conference, 1999. Proceedings of the 1999  (Volume:6 )

Date of Conference:

1999