基于稀疏约束的低复杂度可变分数时延滤波器

Low-Complexity Design of Sparse-Constrained Variable Fractional Delay Filter

针对基于Farrow结构的可变分数时延(Variable fractional delay,VFD)滤波器需求解大量子滤波器系数这一关键问题,本文将稀疏约束理论引入滤波器的权系数优化中,研究具有稀疏系数的Farrow结构滤波器。在极大极小(Minimax)准则下,通过添加L1正则化约束项改进权系数优化模型,在系数(反)对称性基础上进一步增加系数的稀疏度。然后,采用交替方向乘子法(Alternating direction method of multipliers,ADMM)进行权系数迭代求解。仿真实验表明,本文提出的基于稀疏约束的VFD滤波器在保证高延迟精度的同时,乘法器和加法器分别减少了47.69%和58.60%,极大地降低了系统运算量以及复杂度。

Since variable fractional delay(VFD)filter contains a large number of coefficients to be solved,this paper presents a study on sparse-constrained Farrow structure variable fractional delay filter.We add a L1 regularization constraint to further enhance the sparsity based on coefficient symmetry and optimize its frequency response to approximate a desired frequency response in the minimax error sense.In addition,the alternating direction method of multipliers(ADMM)algorithm is used to iteratively obtain the filter coefficients.Simulation experiments demonstrate that the proposed sparse-constrained VFD filter not only ensures high delay accuracy but also reduces the use of multipliers and adders by 47.69% and 58.60% respectively,thus lowering system computation and complexity greatly.