Decentralized Kalman Filter Comparison for Distributed-Parameter Systems: A Case Study for a 1D Heat Conduction Process

Reference

Z. Hidayat, R. Babuška, 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.

Downloads

Bibtex entry

@inproceedings{HidBab:11-028,
author={Z. Hidayat and R. Babu{\v{s}}ka and B. {D}e Schutter and A. N{\'{u}}{\~{n}}ez},
title={Decentralized {Kalman} Filter Comparison for Distributed-Parameter Systems: {A} Case Study for a {1D} Heat Conduction Process},
booktitle={Proceedings of the 16th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA'2011)},
address={Toulouse, France},
month=sep,
year={2011},
doi={10.1109/ETFA.2011.6059054}
}

Go to the publications overview page.
This page is maintained by Bart De Schutter. Last update: February 21, 2026.