北京邮电大学学报

  • EI核心期刊

北京邮电大学学报 ›› 2024, Vol. 47 ›› Issue (1): 94-99.

• 论文 • 上一篇    下一篇

基于P范数的变尺度扩散公平代价算法

火元莲1,徐天赐1,齐永锋2,徐玉荣2,张印1   

  1. 1. 西北师范大学 物理与电子工程学院
    2. 西北师范大学 计算机科学与工程学院
  • 收稿日期:2023-01-06 修回日期:2023-03-30 出版日期:2024-02-26 发布日期:2024-02-26
  • 通讯作者: 火元莲 E-mail:huoyuanlian@163.com

Variable Scale Factor Diffusion Fair Algorithm Based on P-norm

HUO Yuanlian1, XU Tianci1, QI Yongfeng2, XU Yurong2, ZHANG Yin1   

  • Received:2023-01-06 Revised:2023-03-30 Online:2024-02-26 Published:2024-02-26

摘要: 为了进一步提高扩散式自适应滤波算法在不同噪声环境下的性能,提出了一种新的基于 P 范数的变尺度扩散公平代价函数算法。该算法在公平代价函数的基础上,对误差绝对值项加入了 P 范数,同时利用类箕舌线函数构造了一个随误差变化的尺度因子来共同控制算法的陡峭程度,进而使算法拥有更快的收敛速度和更小的稳态误差。在高斯噪声环境以及 Alpha 稳定分布和伯努利-高斯分布的非高斯噪声环境中的仿真结果表明,所提算法拥有更强的鲁棒性和更低的稳态误差,在不同噪声环境下的性能均优于对比算法。

关键词: 自适应滤波, 分布式, 变尺度因子, P范数

Abstract: A new P-norm based variable scale DFAIR diffusion fair cost function algorithm is proposed to further enhance the performance of diffusion based adaptive filtering algorithms in different noise environments. The absolute value of the error term is augmented with a P-norm and a scale factor that varies with the error is established using a tongue-like function to control the steepness of the algorithm. As a result, the convergence speed of the algorithm is accelerated and the steady-state error is reduced. The simulation results in Gaussian noise environments, as well as non-Gaussian noise environments with alpha stable distribution and Bernoulli Gaussian distribution, demonstrate the algorithm has stronger robustness and lower steady-state error. The performance of the proposed algorithm surpasses that of the comparison algorithm in various noise environments.

Key words: adaptive filtering, distributed, variable scale factor, P-norm

中图分类号: