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Most of real-life signals are nonGaussian, therefore the linear parametrization/modeling methods, suitable for Gaussian signals are neither adequate nor optimal. Using a nonlinear approach, we can significantly improve filtering results. However, the price that has to be paid is the complexity of the nonlinear treatment. In this paper we consider the nonlinear Schurtype orthogonal transformations...
We present algorithms for orthogonal Schur parametrization, innovations transformation and stochastic modeling of second-order non-stationary time-series, showing statistical equivalence (in a weak second-order sense) of the parametrized and modeled time-series.
This paper will present new developments in the identification of large scale network connected systems in the framework of subspace methods. Special structures based on Kronecker products will be proposed that give rise to bilinear structured low dimensional optimization problem. The problems of interest for these large scale problems are imaging systems where images are blurred by perturbations...
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