Title: 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.