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Signal processing in non-Gaussian noise environment is addressed in thispaper. For many real-life situations, the additive noise process present in thesystem is found to be dominantly non-Gaussian. The problem of detection andestimation of signals corrupted with non-Gaussian noise is difficult to trackmathematically. In this paper, we present a novel approach for optimaldetection and estimation of signals in non-Gaussian noise. It is demonstratedthat preprocessing of data by the orthogonal polynomial approximation togetherwith the minimum error-variance criterion converts an additive non-Gaussiannoise process into an approximation-error process which is close to Gaussian.The Monte Carlo simulations are presented to test the Gaussian hypothesis basedon the bicoherence of a sequence. The histogram test and the kurtosis test arecarried out to verify the Gaussian hypothesis.

Polynomial Transformation Method for Non-Gaussian Noise Environment