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Identification of State-Space Models for High-Order Linear Systems and Optical Wavefronts


A state-space disturbance model and associated prediction filter for aero-optical wavefronts are described. The model is computed by system identification from a sequence of wavefronts measured in an airborne laboratory. Estimates of the statistics and flow velocity of the wavefront data are shown and can be computed from the matrices in the state-space model without returning to the original data. Numerical results compare velocity values and power spectra computed from the identified state-space model with those computed from the aero- optical data.

A lattice-filter based state-space model is developed for multichannel linear systems. This state space model preserves desirable characteristics of the residual lattice filter, which include order recursiveness, numerical efficiency for high orders, and robustness with respect to numerical computations. The new model is compared to several prominent methods for identification of a high-order system from noisy input-output data.

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