Reference
Zs. Lendek, R. Babuška, and B. De Schutter, "Distributed Kalman
filtering for cascaded systems,"
Engineering Applications of
Artificial Intelligence, vol. 21, no. 3, pp. 457-469, Apr. 2008.
Abstract
The Kalman filter provides an efficient means to estimate the state of a linear
process, so that it minimizes the mean of the squared estimation error.
However, for naturally distributed applications, the construction and tuning of
a centralized observer may present difficulties. Therefore, we propose the
decomposition of a linear process model into a cascade of simpler subsystems
and the use of a Kalman filter to individually estimate the states of these
subsystems. Both a theoretical comparison and simulation examples are
presented. The theoretical results show that the distributed observers, except
for special cases, do not minimize the overall error covariance, and the
distributed observer system is therefore suboptimal. However, in practice, the
performance achieved by the cascaded observers is comparable and in certain
cases even better than the performance of the centralized observer. A
distributed observer system also leads to increased modularity, reduced
complexity, and lower computational costs.
Publisher page
Downloads
BibTeX
@article{LenBab:07-017,
author = {Lendek, {\relax Zs}{\'{o}}fia and Babu{\v{s}}ka, Robert and De
Schutter, Bart},
title = {Distributed {Kalman} Filtering for Cascaded Systems},
journal = {Engineering Applications of Artificial Intelligence},
volume = {21},
number = {3},
pages = {457--469},
month = apr,
year = {2008}
}