NASA STI Repository (National Aeronautics and Space Administration) · 1972 · 22 citations · 1 references
Open access
EngineeringMathematicsSystems EngineeringDynamical SystemsOptimizationSystems AnalysisDynamic Systems AnalysisKalman FilteringParameter EstimationParameter Estimation Under UncertaintyState EstimationSystem IdentificationDynamic System IdentificationFisher InformationFisher Information AnalysisIdentification Of Dynamical SystemsLinear System IdentificationParameter IdentificationState-space EquationsState-space Identification
Identification criteria are presented for linear dynamic systems with and without process noise. With process noise, the state equations are replaced by the Kalman filter equations. If the identification performance index is expanded in a Taylor's series with respect to the parameters to be identified, then maximizing the weighting factor of the quadratic term with respect to the inputs will insure that an identification algorithm will converge more rapidly and to a more accurate result than with nonoptimal inputs. The expectation of this weighting factor is the Fisher information matrix, and its inverse is a lower bound for the covariance of the parameters. Direct and indirect methods of calculating the information matrix are presented for systems with and without process noise.
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