A Family of Variable Step-Size Normalized Sub-Band Adaptive Filter Algorithms using Statistics of System Impulse Response

This paper presents a new Variable Step-Size Normalized Subband Adaptive Filter (VSS-NSAF) algorithm. The proposed algorithm uses the prior knowledge of the system impulse response statistics and the optimal step-size vector is obtained by minimizing the Mean-Square Deviation (MSD). In comparison with NSAF, the VSS-NSAF algorithm has faster convergence speed and lower MSD. To reduce the computational complexity of VSS-NSAF, the VSS Selective Partial Update NSAF (VSS-SPU-NSAF) is proposed where the filter coefficients are partially updated in each subband at every iteration. We demonstrated the good performance of the proposed algorithms in convergence speed and steady-state MSD for a system identification set-up.