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Stochastic Gaussian And Non-Gaussian Signal Modeling, Madhu Kishore Yerramothu

Open Access Theses & Dissertations

This thesis introduces a methodology for modeling stochastic signals that have either Gaussian or approximately bell-shaped non-Gaussian distribution. The synthesized model can be used to generate stochastic signals that approximate both the power spectral density (PSD) and the probability density function (pdf) of the original stochastic signal. The new methodology is based on non-linear transformations, filter banks and autoregressive-moving average (ARMA) models. Because the stochastic signals modeled can have Gaussian distribution or approximately bell shaped non-Gaussian distribution, normality tests such as sample skewness, sample kurtosis, Kolmogorov-Smirnov test, and Shapiro-Wilk test are also used.

Many methods have been proposed in the …