Parallel adaptation for enhanced RLS tracking

A new variable forgetting factor RLS adaptive filter is introduced. This algorithm, based on an accurate new measure of RLS performance in a random walk scenario, attempts to achieve the optimal RLS steady-state misadjustment under the assumption that the Wiener filter coefficients vary according to that nonstationarity. Simulations demonstrate that the proposed method is effective in other nonstationary environments and outperforms existing fixed and variable forgetting factor schemes