Adaptive robust impulse noise filtering

It is well known that when data is contaminated by non-Gaussian noise, conventional linear systems may perform poorly. The paper presents an adaptive robust filter (adaptive preprocessor) for canceling impulsive components when the nominal process (or background noise) is a correlated, possibly nonstationary, Gaussian process. The proposed preprocessor does not require iterative and/or batch processing or prior knowledge about the nominal Gaussian process; consequently, it can be implemented 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 preprocessor to clean the impulsive components in received data samples, conventional linear systems based on the Gaussian assumption can work in an impulsive environment with little if any modification. The technique is applicable to a wide range of problems, such as detection, power spectral estimation, and jamming or clutter suppression in impulsive environments. >