Performance analysis of a split-path LMS adaptive filter for AR modeling

A split-path adaptive filter is proposed for extracting the model parameters of an autoregressive process. The structure is composed of two linear phase filters connected in parallel, one antisymmetric and the other symmetric. The two filters are adapted independently on a sample-by-sample basis using the least-mean-square (LMS) algorithm. The performance of the system in terms of convergence speed and excess mean square error is analyzed in detail, and comparisons with the conventional transversal structure are made. Theoretical analysis and experimental results show that the model can provide a much faster rate of convergence at the expense of only a moderate increase in computation. Two methods for choosing control parameters for the split-path adaptive filter are also suggested to improve further the convergence behavior