分布式子带自适应滤波算法

为了解决分布式最小均方算法在输入信号相关性较高时收敛速度较慢、分布式仿射投影算法计算复杂度较高等问题,本文提出了两种分布式子带自适应滤波算法,即递增式和扩散式子带自适应滤波算法.分布式子带自适应滤波算法将节点信号进行子带分割来降低信号的相关性,从而加快收敛速度.由于用于子带分割的滤波器组中包含了抽取单元,所以分布式子带自适应滤波算法和对应的分布式最小均方算法的计算复杂度相近.仿真结果表明,与分布式最小均方算法相比,分布式子带自适应滤波算法具有更好的收敛性能.

Distributed Subband Adaptive Filtering Algorithms

To address the problem of slow convergence rate of the distributed least mean square algorithms for highly correlated input signals and the problem of high computational complexity of the distributed affine projection algorithms,this paper proposes two distributed subband adaptive filtering algorithms,i.e.,the incremental and diffusion subband adaptive filtering algorithms.The distributed subband adaptive filtering algorithms partition the signals of nodes to reduce their correlation so that their convergence rate can be increased.The computational complexities of the distributed subband adaptive filtering algorithms are close to those of their corresponding distributed least mean square algorithms due to decimation operations included in the filter banks used for subband partition.Simulation results show that the distributed subband adaptive filtering algorithms exhibit good performance as compared to the corresponding distributed least mean square algorithms.