AN FIR CASCADE STRUCTURE FOR ADAPTIVE LINEAR PREDICTION

An alternative structure for adaptive linear prediction is proposed in which the adaptive filter is replaced by a cascade of independently a dapting, low-order stages, and the prediction is generated by means of successive refinements. When the adaptation algorithm for the stages is LMS, the associated short filters are less affected by eigenvalue s pread and mode coupling problems and display a faster convergence to t heir steady-state value. Experimental results show that a cascade of s econd-order LMS filters is capable of successfully modeling most input signals, with a much smaller MSE than LMS or lattice LMS predictors i n the early phase of the adaptation. Other adaptation algorithms can b e used for the single stages, whereas the overall computational cost r emains linear in the number of stages, and very fast tracking is achie ved.