A sliding window RLS-like adaptive algorithm for filtering alpha-stable noise

We introduce a sliding window adaptive RLS-like algorithm for filtering alpha-stable noise. Unlike previously introduced stochastic gradient-type algorithms, the new adaptation algorithm minimizes the L/sub p/ norm of the error exactly in a sliding window of fixed size. Therefore, it behaves much like the RLS algorithm in terms of convergence speed and computational complexity compared to previously introduced stochastic gradient-based algorithms, which behave like the LMS algorithm. It is shown that the new algorithm achieves superior convergence rate at the expense of increased computational complexity.