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- W2015207440 abstract "In this paper, we introduce an estimation error analysis for quadratically nonlinear system identification in the short-time Fourier transform (STFT) domain. The identification scheme consists of a parallel connection of a linear component, represented by crossband filters between subbands, and a quadratic component, which is modeled by multiplicative cross-terms. We mainly concentrate on two types of undermodeling errors. The first is caused by employing a purely linear model in the estimation process (i.e., nonlinear undermodeling), and the second is a consequence of restricting the number of estimated crossband filters in the linear component. We derive analytical relations between the noise level, nonlinearity strength, and the obtainable mean-square error (mse) in subbands. We show that for low signal-to-noise ratio (SNR) conditions, a lower mse is achieved by allowing for nonlinear undermodeling and utilizing a purely linear model. However, as the SNR increases, the performance can be generally improved by incorporating a nonlinear component into the model. We further show that as the SNR increases, a larger number of crossband filters should be estimated to attain a lower mse, whether a linear or nonlinear model is employed. Experimental results support the theoretical derivations." @default.
- W2015207440 created "2016-06-24" @default.
- W2015207440 creator A5005544027 @default.
- W2015207440 creator A5080194185 @default.
- W2015207440 date "2010-12-01" @default.
- W2015207440 modified "2023-09-27" @default.
- W2015207440 title "Undermodeling-Error Quantification for Quadratically Nonlinear System Identification in the Short-Time Fourier Transform Domain" @default.
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- W2015207440 doi "https://doi.org/10.1109/tsp.2010.2068296" @default.
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