The Precompression Processing of LMS Algorithm in Noise Elimination

Pengfei Lin,Ning Zhang,Xingya Wu,Chunsheng Lin

doi:10.1155/2019/2131040

Pengfei Lin, Ning Zhang + Show 2 more

Open Access

https://doi.org/10.1155/2019/2131040

Copy DOI

Abstract

In this study, the authors propose a novel precompression processing (PCP) of the least mean squares (LMS) algorithm based on a regulator factor. The novelty of the PCP algorithm is that the compressed input signals vary from each other on different components at each iteration. The input signal of the improved LMS algorithm is precompressed based on the regulator factor. The precompressed input signal is not only related to the regulator factor α and the current value of the input signal at each iteration but also related to the amplitude of the input signal before this iteration. The improved algorithm can eliminate the influence of input signal mutation on the filter performance. In the numerical simulations, we compare the improved LMS algorithm and NLMS algorithm in the cases of normal input signal and input signal with mutation and the influence of different regulator factors on the noise elimination. Results show that the PCP algorithm has good noise elimination effect when the input signal changes abruptly and the regulator factor α = 0.01 can meet the requirements.

Highlights

Adaptive filters are widely used in system identification, adaptive line spectrum enhancement, echo cancellation, and other fields [1,2,3]
In 1997, Gan proposed a new approach in adjusting the step size of the least mean squares (LMS) using the fuzzy logic technique, and the earlier work was extended by giving a complete design methodology and guidelines for developing a reliable and robust fuzzy step size LMS (FSS-LMS) algorithm [4]
In 2013, Kang et al proposed a new bias-compensated normalized least mean squares (NLMS) algorithm for parameter estimation with a noisy input. e algorithm is obtained from an approximated cost function based on the statistical properties of the input noise, and a condition checking constraint is involved to decide whether the weight coefficient vector must be updated [7]

Summary

Introduction

Adaptive filters are widely used in system identification, adaptive line spectrum enhancement, echo cancellation, and other fields [1,2,3]. E precompression processing (PCP) of the least mean squares (LMS) algorithm based on a regulator factor can reduce the effect of signal mutation on the noise filtering well.

Results

Conclusion