A fast-implemented recursive inverse adaptive filtering algorithm

Mohammad Shukri Ahmad, A. Hocanin, O. Kukrer
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引用次数: 1

Abstract

The recently proposed Recursive Inverse (RI) adaptive algorithm [1] has shown improved performance in channel equalization and system identification settings. Although its computational complexity is lower than those of the RLS and Robust RLS algorithms, its computational complexity can be reduced further. A fast implementation method is applied in this paper to decrease its computational complexity. The performance of the fast implemented RI algorithm is compared to those of the Variable Step-Size LMS (VSSLMS), Discrete Cosine Transform LMS (DCTLMS) and Recursive-Least-Squares (RLS) algorithms in Additive White Gaussian Noise (AWGN), Additive Correlated Gaussian Noise (ACGN), Additive White Impulsive Noise (AWIN) and Additive Correlated Impulsive Noise (ACIN) environments in a noise cancellation setting. Simulation results show that the Fast RI algorithm performs better than the VSSLMS and DCTLMS algorithms. Its performance is the same as in the RLS algorithm with a considerable reduction in complexity.
一种快速实现的递归逆自适应滤波算法
最近提出的递归逆(RI)自适应算法[1]在信道均衡和系统识别设置方面显示出改进的性能。虽然其计算复杂度低于RLS和鲁棒RLS算法,但其计算复杂度可以进一步降低。本文采用了一种快速实现方法来降低其计算复杂度。在噪声消除环境下,将快速实现的RI算法与变步长LMS (VSSLMS)、离散余弦变换LMS (DCTLMS)和递归最小二乘(RLS)算法在加性高斯白噪声(AWGN)、加性相关高斯噪声(ACGN)、加性白脉冲噪声(AWIN)和加性相关脉冲噪声(ACIN)环境下的性能进行了比较。仿真结果表明,Fast RI算法的性能优于VSSLMS和DCTLMS算法。其性能与RLS算法相同,但复杂度大大降低。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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