Subband adaptive filter trained by differential evolution for channel estimation

Abstract

The normalized subband adaptive filter (NSAF) is widely accepted as a preeminent adaptive filtering algorithm because of its efficiency under the colored excitation. However, the convergence rate of NSAF is slow. To address this drawback, in this paper, a variant of the NSAF, called the differential evolution (DE)-NSAF (DE-NSAF), is proposed for channel estimation based on DE strategy. It is worth noticing that there are several papers concerning designing DE strategies for adaptive filter. But their signal models are still the single adaptive filter model rather than the fullband adaptive filter model considered in this paper. Thus, the problem considered in our work is quite different from those. The proposed DE-NSAF algorithm is based on real-valued manipulations and has fast convergence rate for searching the global solution of optimized weight vector. Moreover, a design step of new algorithm is given in detail. Simulation results demonstrate the improved performance of the proposed DE-NSAF algorithm in terms of the convergence rate.