A Proportionate NLMS Algorithm for the Identification of Sparse Bilinear Forms

Abstract

Proportionate-type algorithms are designed to exploit the sparseness character of the systems to be identified, in order to improve the overall convergence of the adaptive filters used in this context. However, when the parameter space is large, the system identification problem becomes more challenging. In this paper, we focus on the identification of bilinear forms, where the bilinear term is defined with respect to the impulse responses of a spatiotemporal model. In this framework, we develop a proportionate normalized least-mean-square algorithm tailored for the identification of such bilinear forms. Simulation results indicate the good performance of the proposed algorithm, in terms of both convergence rate and computational complexity.