In this paper, we derive a modified interacting multiple model filter for jump Markov systems with unknown process and measurement noise covariances. Using the inverse-Wishart distribution as the conjugate prior of noise covariances, the system state together with the noise parameters for each mode are inferred by the variational Bayesian method. The mixing and output estimates are calculated according to the weighted Kullback–Leibler average of mode-conditioned estimates. Simulation results show the effectiveness of the proposed algorithm.
Variational Bayesian IMM-Filter for JMSs With Unknown Noise Covariances
IEEE Transactions on Aerospace and Electronic Systems ; 56 , 2 ; 1652-1661
2020-04-01
1000197 byte
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
Robustness of a Kalman Filter Against Uncertainties of Noise Covariances
British Library Online Contents | 1993
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