α噪声环境下整体最小平均P-范数IIR自适应滤波算法
otal Least Mean lp-Norm Algorithm for adaptive IIR Filtering in α-Stable Noise Environments
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摘要: 当无限冲激响应(IIR)系统输入和输出信号中都存在α稳定噪声干扰,传统的最小平均P-范数算法(LMP)的解会出现较大偏差,本文提出了一种自适应IIR滤波整体最小平均P-范数(IIR_TLMP)算法,算法中整体考虑输入和输出信号的α稳定噪声干扰,将最小化lp范数Rayleigh商采用随机梯度法得到自适应IIR滤波方程。通过仿真首先考察了特征指数和步长因子等主要参数对TLMP算法性能的影响,最后分别在时不变和时变系统中,将TLMP算法与LMP算法的性能在进行了比较,结果显示TLMP有更快的收敛速度和更小的误差。Abstract: When both the input and the output of a IIR system are corrupted by α-stable noises, the classical least mean lp-norm (IIR_LMP) algorithms usually provide a large biased solution. A total least mean lp-norm (TLMP) algorithm for adaptive IIR filtering is discussed in this paper in which α-stable noise interferences of the input and the output are total considered and random gradient method is used in the least lp-norm Rayleigh quotient to obtain the adaptive IIR filtering equation. The simulation is performed to evaluate the influence of the main parameters such as character index and step factor on the performance of the TLMP algorithm. In time-varying system and time-invariant system, the performances of the TLMP algorithm and the LMP algorithm are compared and the results indicate that TLMP algorithm has faster convergence speed and less error.