.png){kind=icon}
Return
A noise resilient variable step-size LMS algorithm
.png){kind=icon}
PRE
AI
DOI：10.1016/j.sigpro.2007.09.015.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
This work presents a modified version of the variable step size (VSS) least mean square (LMS) algorithm originally proposed by Kwong and Johnston [IEEE Trans. Signal Process. 40(7) (July 1992) 1633-1642]. The performance of the new algorithm, called noise resilient variable step size (NRVSS), is less sensitive than VSS to the power of the measurement noise. Its implementation requires only a very small increase in the computational complexity. Analytical models are derived for both NRVSS and VSS algorithms for Gaussian signals and small step-size fluctuations. Simulation results show that the NRVSS algorithm has approximately the same transient behavior as VSS but leads to lower steady-state excess mean-square errors as the signal-to-noise ratio (SNR) decreases. The NRVSS algorithm is specially indicated for adaptive interference reduction in biomedical applications. (C) 2007 Elsevier B.V. All rights reserved.
Keywords:
adaptive filter
LMS
variable step size
VSS
noise suppression
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
3.6
Papers:
9.9K
Citations:
1.7W
Organization
Cited Papers
ShareSaveShareSaveActive Power Control to Mitigate Frequency Deviations in Large-Scale Grid-Connected PV System Using Grid-Forming Single-Stage Inverters.png)
Energies
OAAI
EnergiesIF0
OAAIShareSaveShareSaveShareSaveShareSaveShareSave
View more