Self-tuning Information Fusion Kalman Filter with Input Estimation
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For the multisensor linear discrete time-invariant systems with unknown constant input and unknown noise statistics, the on-line estimators of unknown input and filtering gain are obtained based on CARMA innovation model. For the multisensor stochastic control systems with known input and noise statistics, the optimal information fusion steady-state Kalman filter are presented based on Fadeeva formula. Furthermore, a self-tuning information fusion Kalman filter with input estimation is presented. Based on the dynamic error system analysis method, its asymptotic optimality is proved, i.e. it converges to the optimal fusion steady-state Kalman filter in a realization. A simulation example for a target tracking system with three sensors shows its effectiveness.
KeywordsMultisensor information fusion input estimation self-tuning Kalman filter convergence asymptotic optimality
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