Model Structure Determination and Identifiability Problems in System Identification.
SYSTEMS CONTROL INC PALO ALTO CALIF
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The canonical structure of linear systems is examined and specific canonical forms are constructed. It is shown that although a general stochastic model is not identifiable, its associated steady-state kalman filter is identifiable if a canonical form is used. A non-iterative method is developed for estimating the parameters including model order and noise covariance of a steady-state Kalman filter. Finally, the concept of local identifiability is discussed and sufficient conditions are derived for local identifiability of parameters in terms of the Fisher information matrix. Author
- Statistics and Probability