DocumentCode :
821962
Title :
New smoothing algorithms based on reversed-time lumped models
Author :
Sidhu, Gursharan S. ; Desai, Uday B.
Author_Institution :
State University of New York at Buffalo, Ahmerst, NY, USA
Volume :
21
Issue :
4
fYear :
1976
fDate :
8/1/1976 12:00:00 AM
Firstpage :
538
Lastpage :
541
Abstract :
Corresponding to a process 
with a known state model propagating in growing time, we obtain a process 
, statistically equivalent to 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.
with a known state model propagating in growing time, we obtain a process , statistically equivalent to 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.Keywords :
Least-squares estimation; Linear systems, stochastic continuous-time; Linear systems, stochastic discrete-time; Markov processes; Recursive estimation; Smoothing methods; State estimation; Additives; Covariance matrix; Hidden Markov models; Kalman filters; Recursive estimation; Reflection; Riccati equations; Smoothing methods; State estimation;
fLanguage :
English
Journal_Title :
Automatic Control, IEEE Transactions on
Publisher :
ieee
ISSN :
0018-9286
Type :
jour
DOI :
10.1109/TAC.1976.1101289
Filename :
1101289
Link To Document :
