In correlated non-Gaussian clutter back- grounds, serious degradation occurs in traditional clutter suppression method. A new clutter suppression method was proposed based on Symmetric alpha stable (SαS) frac- tional...
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In correlated non-Gaussian clutter back- grounds, serious degradation occurs in traditional clutter suppression method. A new clutter suppression method was proposed based on Symmetric alpha stable (SαS) frac- tional autoregressive model. The SαS fractional autore- gressive model was considered as a stochastic processed in which a fractional autoregressive system was driven by a white SαS noise, and the clutter suppression filter was established by using the model parameters which was es- timated based on the Fractional lower order covariance (FLOC). The SαS fractional autoregressive model can ef- fectively describe the non-Gaussian characteristics as well as the long and short correlation characteristics of the clut- ter, and by using FLOC, more accurate parameters can be estimated. Simulations and real data results show that the proposed method obviously outperforms the traditional clutter suppression method under correlated non-Gaussian clutter backgrounds.
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