Analysis of incremental augmented affine projection algorithm for distributed estimation of complex signals
For researchers in distributed signal processing, this work provides an incremental algorithm that handles both proper and improper complex signals, but it is an incremental extension of existing methods.
The paper proposes an incremental augmented affine projection algorithm (incAAPA) for distributed estimation in incremental networks with widely linear complex signals, achieving improved estimation performance by exploiting full second-order statistics and spatio-temporal diversity. Simulations validate the theoretical analysis and show good performance.
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 order circular (proper) and noncircular (improper) signals. The validity of the theoretical results and the good performance of the proposed algorithm are demonstrated by several computer simulations.