The family of affine projection algorithm with dynamic selection of input vectors

To improve the convergence rate, multiple input vectors are used to update the filter coefficients in the affine projection algorithm (APA) family, however, different input vector affects the convergence differently. The ideal selection criterion of the input vectors is derived by the largest decrease of the mean-square deviation. This selection condition fits for different algorithms of the APA family. Based on the ideal selection criterion, a family of APAs which dynamically select input vectors is presented. The experimental results show that the proposed algorithms have fast convergence speed, small steady-state error compared to the conventional APAs. In addition, the proposed algorithms retain low overall computational complexity.