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State-Space Model and Kalman Filter Gain Identification by a Kalman Filter of a Kalman Filter
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This paper describes an algorithm that identifies a state-space model and an associated steady-state Kalman filter gain from noise-corrupted input–output data. The model structure involves two Kalman filters where a second Kalman filter accounts for the error in the estimated residual of the first Kalman filter. Both Kalman filter gains and the system state-space model are identified simultaneously. Knowledge of the noise covariances is not required.
Title: State-Space Model and Kalman Filter Gain Identification by a Kalman Filter of a Kalman Filter
Description:
This paper describes an algorithm that identifies a state-space model and an associated steady-state Kalman filter gain from noise-corrupted input–output data.
The model structure involves two Kalman filters where a second Kalman filter accounts for the error in the estimated residual of the first Kalman filter.
Both Kalman filter gains and the system state-space model are identified simultaneously.
Knowledge of the noise covariances is not required.
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