Keywords
linear systems, jump inputs, Kalman filter, empirical Bayes detection
Abstract
A decision-directed approach is presented for analyzing linear systems with unknown jump inputs. The system model parameters are estimated using a Kalman filter, and an empirical Bayes detection procedure is introduced to set the detector parameters, resulting in a decision-directed generalized likelihood ratio test coupled with recursive system parameter estimation. Monte Carlo results are presented to validate the performance of the algorithm.
Original Publication Citation
Stirling, W. "Simultaneous System Identification and Decision-Directed Detection and Estimation of Jump Inputs to Linear Systems." Automatic Control, IEEE Transactions on 32.1 (1987): 86-9
BYU ScholarsArchive Citation
Stirling, Wynn C., "Simultaneous system identification and decision-directed detection and estimation of jump inputs to linear systems" (1987). Faculty Publications. 748.
https://scholarsarchive.byu.edu/facpub/748
Document Type
Peer-Reviewed Article
Publication Date
1987-01-01
Permanent URL
http://hdl.lib.byu.edu/1877/1106
Publisher
IEEE
Language
English
College
Ira A. Fulton College of Engineering and Technology
Department
Electrical and Computer Engineering
Copyright Status
© 1987 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.
Copyright Use Information
http://lib.byu.edu/about/copyright/