It is well known that when data is contaminated by non-Gaussian noise,
conventional linear systems may perform poorly, This paper presents a
n adaptive robust filter (adaptive preprocessor) for canceling impulsi
ve components when the nominal process (or background noise) is a corr
elated, possibly nonstationary, Gaussian process. The proposed preproc
essor does not require iterative and/or batch processing or prior know
ledge about the nominal Gaussian process; consequently, it can be impl
emented in real time and adapt to changes in the environment. Based on
simulation results, the proposed adaptive preprocessor shows superior
performances over presently available techniques for cleaning impulse
noise, Using the proposed adaptive pre-processor to clean the impulsi
ve components in received data samples, conventional linear systems ba
sed on the Gaussian assumption can work in an impulsive environment wi
th little if any modification, The technique is applicable to a wide r
ange of problems, such as detection, power spectral estimation, and ja
mming or clutter suppression in impulsive environments.