An Improved MPNLMS Algorithm for Echo Cancellation in Digital Video Telephone System

In this paper, an improved proportionate normalized least mean square (MPNLMS) algorithm incorporating a data selection strategy based on the set-membership concept is introduced. Proportionate adaptive filters can improve the convergence speed for the identification of sparse systems as compared to their conventional counterparts. Set-membership filtering (SMF) combine a bounded error specification on the adaptive filter with the concept of data-reusing. The resulting algorithms have low average computational complexity because coefficient update is not performed at each iteration. Thereafter, the ideas of the partial-update MPNLMS algorithms found in the literature are incorporated in the framework of set-membership filtering, from which data-selective MPNLMS algorithms with partial-update are derived. The resulting algorithm is flexible in the sense that it allows more general tradeoff between speed of convergence and misadjustment while constraining the overall computational complexity. Simulations show good results in terms of reduced number of updates, speed of convergence, and final meansquared error. Objective tests are made using Composite Source Signals(CSS) according to ITU-T recommendation G.168. The performances meet with G.168 well. o and accurately calculate to-be-detected signal frequency.