Improved multiband structured subband adaptive filter algorithm with L0-norm regularization for sparse system identification

Abstract

The improved multiband structured subband adaptive filter (IMSAF) algorithm improves the performance of normalized subband adaptive filter (NSAF) algorithm by employing the recent regressors at each subband. The present study introduces the IMSAF algorithm for sparse system identification. The L0-norm regularization term is applied to the proposed cost function of IMSAF and the L0-IMSAF is established. The L0-IMSAF has significantly better convergence speed than conventional IMSAF. In the following, the theoretical steady-state performance analysis of the L0-IMSAF is presented. To reduce the computational complexity of the L0-IMSAF, the selective regressor (SR) and the dynamic selective regressor (DSR) strategies are utilized and L0-SR-IMSAF and L0-DSR-IMSAF are proposed. The approaches in L0-SR-IMSAF and L0-DSR-IMSAF algorithms are based on the selection of the regressors at each subband. The L0-SR-IMSAF and L0-DSR-IMSAF have good convergence speed, low steady-state error, and low computational complexity features. The good performances of the proposed algorithms are demonstrated through several simulation results in sparse system identification.