Showing papers by "Kenneth Steiglitz published in 1967"

Power-spectrum identification in terms of rational models

Abstract: A technique is described for the identification of unknown power-spectral densities from sampled data in terms of a rational function of z The problem is reduced to the minimization of a function of K parameters, where K is the order of the numerator of the model This criterion, called the "minimum residual" criterion, reduces to the maximum likelihood criterion when the observed signal is Gaussian A computational technique is described for minimizing this function which uses filtering and correlation to obtain the gradient and an iterative descent method due to M J D Powell for minimization Some computational results are given in which the method is compared with all-pole and conventional spectrum estimation techniques

Maximum likelihood estimation of rational transfer function parameters

Abstract: The problem of estimating unknown transfer function parameters from finite input-output records which have been disturbed by additive Gaussian noise with unknown correlation is considered. A rational sampled-data model of preselected order is assumed appropriate, and following the work of Klein, Astrom and Bohlin, and Mayne, the likelihood function is generated from the data by numerical filtering. The maximum likelihood criterion leads to nonlinear regression equations for the unknown parameters, which are solved by damped Gauss-Newton iteration. Some computational experiments are described.