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A new recursive linear filter is considered for the estimation of the unknown states of a dynamic system. In general, this new filter has fast convergence rate and lower RMS error than the conventional Kalman filter during the transient phase. It becomes a Wiener filter as time increases.
This paper deals with the estimation of the unmeasurable states of a linear, time-invariant dynamic system by first identifying the initial state of the system. This new approach makes use of the state transition concept to predict the future behavior of the system. A state estimator which operates in series with the initial state identifier is developed. Satisfactory state estimates have been obtained...
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