Model Structure Determination and Identifiability Problems in System Identification.
Abstract
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)
Document Details
- Document Type
- Technical Report
- Publication Date
- Feb 01, 1973
- Accession Number
- AD0756271
Entities
People
- Edison T. S. Tse
- Howard L. Weinert
- John J. Anton
- Raman K. Mehra