Optimal, predictive, and adaptive control
Optimal, predictive, and adaptive control
System identification (2nd ed.): theory for the user
System identification (2nd ed.): theory for the user
Maximum likelihood estimation via the extended covariance and combined square-root filters
Mathematics and Computers in Simulation
On efficient parametric identification methods for linear discrete stochastic systems
Automation and Remote Control
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We consider an application of the auxiliary quality functional (AQF) method for identifying the parameters of a linear dynamical system in case when the filtering is done with a square root covariance filter. We construct a new algorithm for computing the gradient of the auxiliary quality functional. The advantages of this algorithm are that it is stable to computer rounding errors and does not require the user to write down the "differentiated" Kalman filter in the standard form for every unknown system parameter. All values necessary to compute the values of the AQF gradient are computed in terms of the square root covariance filter with orthogonal transformations.