We show that a new design criterion, i.e., the least squares on subband errors regularized by a weighted norm, can be used to generalize the proportionate-type normalized subband adaptive filtering (PtNSAF) framework. The new criterion directly penalizes subband errors and includes a sparsity penalty term which is minimized using the damped regularized Newton's method. The impact of the proposed generalized PtNSAF (GPtNSAF) is studied for the system identification problem via computer simulations. Specifically, we study the effects of using different numbers of subbands and various sparsity penalty terms for quasi-sparse, sparse, and dispersive systems. The results show that the benefit of increasing the number of subbands is larger than promoting sparsity of the estimated filter coefficients when the target system is quasi-sparse or dispersive. On the other hand, for sparse target systems, promoting sparsity becomes more important. More importantly, the two aspects provide complementary and additive benefits to the GPtNSAF for speeding up convergence.
翻译:我们显示,一个新的设计标准,即按加权规范对子带错误进行正规化的最小方格,可以用来概括比例型常规子带适应性过滤(PtNSAF)框架。新的标准直接惩罚子带错误,并包括一个宽度惩罚术语,使用被割断的正规化牛顿法将这一术语降到最低。拟议的通用PtNSAF(GPTNSAF)的影响是通过计算机模拟研究系统识别问题。具体地说,我们研究使用不同数量的子带和各种宽度惩罚条款对准散散散、稀散和分散系统的影响。结果显示,在目标系统半散或分散时,增加子带数量的好处大于促进估计过滤系数的松散性。另一方面,对于稀疏的目标系统,促进恐慌性则更为重要。更重要的是,这两个方面为GPTNSAF加速趋同提供了补充和添加性好处。