IEEE Transactions on Automatic Control · 2011 · 161 citations · 28 references
ComputingSystems EngineeringDistributed SystemsDynamical SystemsSystem DynamicsConsensus AlgorithmsDistributed ConsensusDistributed ControlKalman FilteringSensor NetworksState EstimationConsensusDistributed EstimationDistributed State EstimationDistributed TrackingFilteringMarkov Jump SystemsUnscented Kalman FilterUnscented Kalman Filtering
This note studies the problem of distributed estimation for jump Markov nonlinear systems (JMNLSs) in a not fully connected sensor network. Based on the consensus theory, a distributed unscented Kalman filter (UKF) is first derived for nonlinear systems without jumping parameters and then it is extended to develop a distributed multiple model UKF for JMNLSs. The proposed filtering algorithm is illustrated via a simulation example involving tracking a maneuvering target.
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