Analysis of Incremental Augmented Affine Projection Algorithm for Distributed Estimation of Complex-Valued Signals

Abstract

This paper considers the problem of distributed estimation in an incremental network when the measurements taken by the node follow a widely linear model. The proposed algorithm which we refer to it as incremental augmented affine projection algorithm (incAAPA) utilizes the full second order statistical information in the complex domain. Moreover, it exploits spatio-temporal diversity to improve the estimation performance. We derive steady-state performance metric of the incAAPA in terms of the mean-square deviation (MSD).We further derive sufficient conditions to ensure mean-square convergence. Our analysis illustrate that the proposed algorithm is able to process both second

An Adaptive Filtering Algorithm for Non-Gaussian Signals in Alpha-Stable Distribution

“…The weighting factor β (n, i) was introduced to make the filter track the changes of observed data under non-stationary environment [31]:…”