Sparsity-aware normalized subband adaptive filters with jointly optimized parameters

Abstract

The normalized subband adaptive filter (NSAF) has a faster convergence rate than the NLMS adaptive filter when the input signal is correlated. Recently some sparsity-aware NSAFs (SA-NSAFs) were presented, which make use of the sparsity of the unknown system to accelerate convergence rate or reduce the steady-state misalignment. However, like the NSAF they also need to take a tradeoff between fast convergence rate and small steady-state misalignment. To address this problem, this paper proposes to jointly optimize the step-size and intensity factor of the SA-NSAFs. Another advantage of the presented method is that it can solve the problem of selecting the optimal intensity factor for the SA-NSAFs with repeated manual attempts. The parameter optimization is achieved by minimizing the mean-square deviation (MSD). Simulation results are provided to show the superiority of the proposed algorithms.