Affine projection algorithm with selective regressors

Affine projection algorithm, which updates the weight vector based on several previous input vectors, is an useful adaptive filter to improve the convergence speed of LMS-type filter. However, the computational complexity of adaptation algorithm highly depends on the number of input vectors used for update. In this paper, we propose affine projection algorithm with selective regressors whose purpose is to reduce complexity by selecting a subset of input regressors at every iteration. The optimal selection of input regressors is derived by comparing the cost functions based on the principle of minimum disturbance. The new algorithms show good convergence performance as attested to by various experimental results.