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.