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Nonlinear smoothing identification algorithm with application to data consistency checksA parameter identification algorithm for nonlinear systems is presented. It is based on smoothing test data with successively improved sets of model parameters. The smoothing, which is iterative, provides all of the information needed to compute the gradients of the smoothing performance measure with respect to the parameters. The parameters are updated using a quasi-Newton procedure, until convergence is achieved. The advantage of this algorithm over standard maximum likelihood identification algorithms is the computational savings in calculating the gradient. This algorithm was used for flight-test data consistency checks based on a nonlinear model of aircraft kinematics. Measurement biases and scale factors were identified. The advantages of the presented algorithm and model are discussed.
Document ID
19930048004
Acquisition Source
Legacy CDMS
Document Type
Reprint (Version printed in journal)
Authors
Idan, M.
(Technion - Israel Inst. of Technology Haifa, United States)
Date Acquired
August 16, 2013
Publication Date
April 1, 1993
Publication Information
Publication: Journal of Guidance, Control, and Dynamics
Volume: 16
Issue: 2
ISSN: 0731-5090
Subject Category
Numerical Analysis
Accession Number
93A32001
Funding Number(s)
CONTRACT_GRANT: NAG2-106
Distribution Limits
Public
Copyright
Other

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