DocumentCode
799116
Title
Stochastic peak tracking and the Kalman filter
Author
Chang, Silvia
Author_Institution
State University of New York, Stony Brook, NY, USA
Volume
13
Issue
6
fYear
1968
fDate
12/1/1968 12:00:00 AM
Firstpage
750
Lastpage
750
Abstract
The peak tracking problem can be reduced to a Kalman falter problem [1] with the additional variable of the excursion amplitude
, which is then obtained by maximizing the expected peak. In the special case where the parameters do not change, the method yields two tracking procedures depending on the criterion used: 1) Tracking for a limited time and then settling for the parameter value so determined. It is shown that the expected error is proportional to t-1, where
is the tracking time [2]. 2) A procedure which agrees with the Kiefer-Wolfowitz stochastic approximation method [3]. It is shown further that the expected total reduction in peak value (due to error and hunting loss) is proportional to 
, which is then obtained by maximizing the expected peak. In the special case where the parameters do not change, the method yields two tracking procedures depending on the criterion used: 1) Tracking for a limited time and then settling for the parameter value so determined. It is shown that the expected error is proportional to t-1, where
is the tracking time [2]. 2) A procedure which agrees with the Kiefer-Wolfowitz stochastic approximation method [3]. It is shown further that the expected total reduction in peak value (due to error and hunting loss) is proportional to 
Keywords
Kalman filtering; Stochastic processes; Tracking filters; Approximation methods; Equations; Kalman filters; Maximum likelihood detection; Optimal control; Stochastic processes; Yield estimation;
fLanguage
English
Journal_Title
Automatic Control, IEEE Transactions on
Publisher
ieee
ISSN
0018-9286
Type
jour
DOI
10.1109/TAC.1968.1099058
Filename
1099058
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