Scalable Distributed Kalman Filtering for Mass-Spring Systems

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.