Reference:
Zs. Lendek,
R. Babuska, 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.