linear systems, jump inputs, Kalman filter, empirical Bayes detection
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). All Faculty Publications. 748.
Ira A. Fulton College of Engineering and Technology
Electrical and Computer Engineering
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