Identification of State-Space Parameters in the Presence of Uncertain Nuisance Parameters

A methodology is presented to account for the uncertainty in maximum likelihood estimates of state space parameters in the presence of uncertain nuisance parameters. The technique uses the asymptotic normality of the uncertainty in the estimates and the implicit function theorem to determine a correction to the estimate uncertainty evaluated from the Fisher information matrix. Efficient evaluation of the correction using Kalman filters is discussed and a numerical example for the X-22A aircraft is presented.