 We find that the extended Kalman filter with such process noise covariance update can be optimal in the sense that the innovation sequence becomes white and in achieving  balance between the mean square estimation error and parameter convergence time.
<fluency>  We find that the extended Kalman filter with such process noise covariance update can be optimal in the sense that the innovation sequence becomes white and in achieving  balance between the mean square estimation error and parameter convergence time.
 We find that the Extended Kalman Filter with such process noise covariance update can be optimal in the sense that the innovation sequence becomes white and in achieving balance between the mean square estimation error and parameter convergence time.