Analysis of a set-membership affine projection algorithm in nonstationary environment

Set-membership (SM) adaptive filters have data-selective co-efficient update leading to lower computational complexity and power consumption. The set-membership affine projection (SM-AP) algorithm has been known for not trading convergence speed with misadjustment and computation complexity. In this paper analytical results related to the SM-AP algorithm in nonstationary environments are advanced. The analysis results for the excess of mean square error (MSE) in nonstationary environments are shown to be quite accurate confirming the attractive features of the SM-AP algorithms.