Reference:
Z. Hidayat,
R. Babuska,
B. De Schutter, and
A. Núñez,
"Decentralized Kalman filter comparison for distributed-parameter
systems: A case study for a 1D heat conduction process,"
Proceedings of the 16th IEEE International Conference on Emerging
Technologies and Factory Automation (ETFA'2011), Toulouse,
France, 8 pp., Sept. 2011.
Abstract:
In this paper we compare four methods for decentralized Kalman
filtering for distributed-parameter systems, which after spatial and
temporal discretization, result in large-scale linear discrete-time
systems. These methods are: parallel information filter, distributed
information filter, distributed Kalman filter with consensus filter,
and distributed Kalman filter with weighted averaging. These filters
are suitable for sensor networks, where the sensor nodes perform not
only sensing and computations, but also communicate estimates among
each other. We consider an application of sensor networks to a heat
conduction process. The performance of the decentralized filters is
evaluated and compared to the centralized Kalman filter.