Distributed Kalman Filtering Using Weighted Averaging
Year: 2006
Type: conference-paper
Abstract: 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.
Authors: Peter Alriksson, Anders Rantzer
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W1711140718