Distributed receding horizon filtering in discrete-time dynamic systems

Authors:
II Young Song;Vladimir Shin
Affiliations:
School of Information and Mechatronics, Gwangju Institute of Science and Technology, Gwangju, South Korea;School of Information and Mechatronics, Gwangju Institute of Science and Technology, Gwangju, South Korea
Venue:
CIRA'09 Proceedings of the 8th IEEE international conference on Computational intelligence in robotics and automation
Year:
2009

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Abstract

A distributed receding horizon filtering for discrete-time dynamic systems is proposed. A distributed fusion with the weighted sum structure is applied to the set of local receding horizon Kalman fIlters (LRHKFs). All LRHKFs have the same receding horizon length. The distributed fusion algorithm represents the optimal linear fusion by weighting matrices under the minimum mean square criterion. In other to compute the optimal matrix weights, the recursive equations for error cross-covariances between the LRHKFs are denved. Simulation example for the tracking system with three sensors demonstrates effectiveness ofthe proposed filter.