Distributed Kalman Filtering Using Weighted Averaging

Författare

Peter Alriksson
Anders Rantzer

Summary, in English

This paper addresses the problem of distributed Kalman filtering, with

focus on limiting the required communication bandwidth.

By distributed we refer to a scenario when all nodes in the network desire an

estimate of the full state of the observed system and there is no

centralized computation center. Communication only takes place

between neighbors and only a fixed number of times each sample. To

reduce bandwidth requirements of individual nodes, estimates

instead of measurements are communicated. A new estimate is

then formed as a weighted average of the neighbouring estimates. The

weights are optimized to yield a small estimation error covariance in

stationarity. The minimization can be done off line thus allowing

only estimates to be communicated. The advantage of communicating

estimates instead of measurements becomes more evident when the number

of nodes exceeds the size of the state vector to be estimated. The

algorithm is applied to one

simple second order system and temperature sensing network.

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