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New smoothing algorithms based on reversed-time lumped models

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2 Author(s)
G. Sidhu ; State University of New York at Buffalo, Ahmerst, NY, USA ; U. Desai

Corresponding to a process x(.) with a known state model propagating in growing time, we obtain a process x_{r}(.) , statistically equivalent to x(.) up to second-order properties but with a state model propagating in reversed time. This result is exploited to obtain recursive linear least-squares estimation algorithms that evolve backwards in time. The reversed-time model is shown to be closely related to the system adjoint of the original state model. Some operator-theoretic consequences are also noted.

Published in:

IEEE Transactions on Automatic Control  (Volume:21 ,  Issue: 4 )