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

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 L-0-norm regularization term is applied to the proposed cost function of IMSAF and the L-0-IMSAF is established. The L-0-IMSAF has significantly better convergence speed than conventional IMSAF. In the following, the theoretical steady-state performance analysis of the L-0-IMSAF is presented. To reduce the computational complexity of the L-0-IMSAF, the selective regressor (SR) and the dynamic selective regressor (DSR) strategies are utilized and L0-SR-IMSAF and L-0-DSR-IMSAF are proposed. The approaches in L-0-SR-IMSAF and L-0-DSR-IMSAF algorithms are based on the selection of the regressors at each subband. The L-0-SR-IMSAF and L-0-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.(C) 2021 Elsevier Inc. All rights reserved.