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Optimal sampling schedule with unknown but bounded measurement errors: Families of linear models

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3 Author(s)
G. Belforte ; Politecnico di Torino, Torino, Italy ; B. Bona ; S. Frediani

The problem of optimal sampling design for parameter estimation when data are generated by linear models is addressed. The measurements are assumed to be corrupted by unknown-but-bounded additive noise. The sampling design assumes that the samples number is free and no replication is allowed. Two main results are shown: 1) for particular classes of linear models, the optimal measurements number is equal to the parameters number, as in the statistical context; 2) the parameters uncertainty intervals of an actual realization are bounded from above by quantities that can be computed a priori, knowing only the model and the error structure.

Published in:

Decision and Control, 1984. The 23rd IEEE Conference on

Date of Conference:

12-14 Dec. 1984