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Identification of Linear Systems with Nonlinear Distortions
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TLDR
A theoretical framework is proposed that extends the linear system description to include the impact of nonlinear distortions: the nonlinear system is replaced by a linear model plus a 'nonlinear noise source'.Abstract:
This paper studies the impact of nonlinear distortions on linear system identification. It collects a number of previously published methods in a fully integrated approach to measure and model these systems from experimental data. First a theoretical framework is proposed that extends the linear system description to include the impact of nonlinear distortions: the nonlinear system is replaced by a linear model plus a 'nonlinear noise source'. The class of nonlinear systems covered by this approach is described and the properties of the extended linear representation are studied. These results are used to design the experiments; to detect the level of the nonlinear distortions; to measure efficiently the 'best' linear approximation; to reveal the even or odd nature of the nonlinearity; to identify a parametric linear model; and to improve the model selection procedures in the presence of nonlinear distortions.read more
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TL;DR: It is shown that the best linear approximation of the MIMO LNL system in the mean square sense can be obtained by the orthogonal projection (ORT) subspace identification method.
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Detection of nonlinear distortions with multisine excitations in the case of nonideal behavior of the input signal
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Proceedings ArticleDOI
The detection of and compensation for nonlinear effects using periodic input signals
TL;DR: In this paper, the identification of linear dynamics in the presence of nonlinear effects is discussed and the first step in such a process, the detection of the nonlinear relationship, is shown to be impossible using the conventional coherence function calculation in conjunction with a periodic excitation.