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Scalable Distributed Kalman Filtering for Mass-Spring Systems

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Author

Summary, in English

This paper considers Kalman Filtering for massspring systems. The aim is a scalable distributed implementation where nodes communicate in a sparse pattern and the state estimate for each node is available locally and usable for control. The focus is on translation invariant systems, to make use of the powerful results available based on Fourier Transform methods. In this case it is known that Kalman Filters will have a coupling that asymptotically falls off exponentially with distance. Examples are shown where the Kalman Filter gains can be truncated very narrowly with small performance loss even though the coupling falls off slowly. A step towards spatially varying systems is taken in analyzing a system with periodically placed sensors, and it is shown that the original design is insensitive to this spatial variation.

Publishing year

2007

Language

English

Document type

Conference paper

Topic

  • Control Engineering

Keywords

  • Kalman Filtering
  • distributed estimation
  • flexible structures

Conference name

46th IEEE Conference on Decision and Control, 2007

Conference date

2007-12-12 - 2007-12-14

Conference place

New Orleans, LA, United States

Status

Published