Decentralized Kalman filter comparison for distributed-parameter systems: A case study for a 1D heat conduction process

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...

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Bibliographic Details
Main Authors: Hidayat, Z., Babuska, R., De Schutter, B., Nunez, A.
Format: Conference Proceeding
Language:English
Subjects:
Covariance matrix
Equations
Estimation error
Kalman filters
Mathematical model
Noise measurement
Time measurement
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Summary: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.
ISSN:1946-0740
1946-0759
DOI:10.1109/ETFA.2011.6059054