ADAPTIVE ROBUST IMPULSE NOISE FILTERING

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.