Nonlinear RLS algorithm using variable forgetting factor in mixture noise

In an impulsive noise environment, most learning algorithms encounter difficulty in distinguishing the nature of a large error signal, whether caused by the impulse noise or model error. Consequently, they suffer from large misadjustment or otherwise slow convergence. A new nonlinear RLS (VFF-NRLS) adaptive algorithm with variable forgetting factor for FIR filters is introduced. In this algorithm, the autocorrelations of non-zero lags, which is insensitive to white noise, is used to control the forgetting factor of the nonlinear RLS. This scheme makes the algorithm have fast tracking capability and small misadjustment. By experimental results, it is shown that the new algorithm can outperform other RLS algorithms