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.

当输入信号相关时，归一化子带自适应滤波器（NSAF）的收敛速度比NLMS自适应滤波器快。最近，提出了一些稀疏感知的NSAF（SA-NSAF），它们利用未知系统的稀疏性来加快收敛速度​​或减少稳态失准。但是，像NSAF一样，它们还需要在快速收敛速度和较小的稳态失准之间进行权衡。为了解决这个问题，本文提出了联合优化SA-NSAFs的步长和强度因子的方法。提出的方法的另一个优点是它可以解决通过反复手动尝试为SA-NSAF选择最佳强度因子的问题。通过最小化均方差（MSD）来实现参数优化。