  Signal processing in non-Gaussian noise environment is addressed in this
paper. For many real-life situations, the additive noise process present in the
system is found to be dominantly non-Gaussian. The problem of detection and
estimation of signals corrupted with non-Gaussian noise is difficult to track
mathematically. In this paper, we present a novel approach for optimal
detection and estimation of signals in non-Gaussian noise. It is demonstrated
that preprocessing of data by the orthogonal polynomial approximation together
with the minimum error-variance criterion converts an additive non-Gaussian
noise process into an approximation-error process which is close to Gaussian.
The Monte Carlo simulations are presented to test the Gaussian hypothesis based
on the bicoherence of a sequence. The histogram test and the kurtosis test are
carried out to verify the Gaussian hypothesis.
