Affine Projection Algorithm Based on Least Mean Fourth Algorithm for System Identification
Xiaoding Wang, Jun Han
Abstract
In the field of signal processing such as system identification, the affine projection algorithm (APA) is extensively implemented. However, running such algorithms in a non-Gaussian scenario may degrade its performance, since the second-order moment cannot extract all information from the signal. To prevent performance degradation of the algorithm in system identification tasks, we propose a novel APA based on least mean fourth (LMF) algorithm. The new algorithm, namely affine projection least mean fourth algorithm (APLMFA) is based on the high-order error power (HOEP) criterion and as such, can achieve improved performance. We also provide a convergence analysis for APLMFA. Numerical simulation results verify the presented APLMFA achieves smaller steady-state error as compared with the state-of-the-art algorithms.