DIstributed Kalman Filtering Using The Internal Model Average Consensus Estimator

We apply the internal model average consensus estimator in [1] to distributed Kalman filtering. The resulting distributed Kalman filter and the embedded average consensus estimator update at the same frequency. We show that if the internal model average consensus estimator is stable, the estimation...

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Bibliographic Details
Published inProceedings of the 2011 American Control Conference pp. 1500 - 1505
Main Authors He Bai, Freeman, Randy A., Lynch, Kevin M.
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.06.2011
Subjects
Estimation error
Frequency estimation
Kalman filters
Noise
Polynomials
Steady-state
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ISBN1457700808
9781457700804
ISSN0743-1619
DOI10.1109/ACC.2011.5991484

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Summary:We apply the internal model average consensus estimator in [1] to distributed Kalman filtering. The resulting distributed Kalman filter and the embedded average consensus estimator update at the same frequency. We show that if the internal model average consensus estimator is stable, the estimation error of the distributed Kalman filter is zero mean in steady state and has bounded covariance even when the dynamical system to be estimated is neutrally stable or unstable.
ISBN:1457700808
9781457700804
ISSN:0743-1619
DOI:10.1109/ACC.2011.5991484