Fast LMS-based Compound Algorithm for Modelling Long Memory Filters with Colored Signal

A new adaptive modelling technique (AP-NCD) is suggested to increase the speed and accuracy of modelling the long memory filter with strong correlation signals by integrating the alternating projection principle with the nonlinear multi-degree of freedom algorithm (NLMDF). The basic idea is to partition a huge filter into a set of sub filters of length m = 2 by using the notion of alternating projection, and then iterate through each sub filter using the NCD method derived from the NLMDF algorithm. The convergence characteristics of the AP-NCD algorithm are investigated in this study, and the principle of parameter selection is derived. The simulation results demonstrate that the AP-NCD algorithm has a faster convergence time and accuracy than the NLMDF, TPLMS, and DCTLMS algorithms when the high correlation input signal is used to model the long memory filter (FIR).