NDLI: Improved Filtered-x Least Mean Kurtosis Algorithm for Active Noise Control

Content Provider	Springer Nature Link
Author	Lu, Lu Zhao, Haiquan
Copyright Year	2016
Abstract	The least mean kurtosis (LMK) algorithm has been successfully applied to linear system identification. It outperforms the conventional least mean square method for Gaussian and non-Gaussian noises. The purpose of this work is to apply the LMK algorithm to active noise control (ANC) systems, i.e., to develop a filtered-x LMK (FxLMK) algorithm. The proposed FxLMK algorithm is robust against the attenuation of various noise interferences in ANC system. To further improve the noise reduction performance, a recursive sampled variance method is incorporated into the FxLMK algorithm, resulting in an improved FxLMK (IFxLMK) algorithm without significantly increasing the computational burden. Simulations in the various noise inputs show the effectiveness of the proposed algorithms. Moreover, the traction substation noise control problem is considered in our simulations, and an overall system scheme and simulation studies are conducted. Simulation results demonstrate that the IFxLMK algorithm can improve the convergence rate of adaptive filter and improve the performance of noise reduction.
Starting Page	1586
Ending Page	1603
Page Count	18
File Format	PDF
ISSN	0278081X
Journal	Circuits, Systems, and Signal Processing
Volume Number	36
Issue Number	4
e-ISSN	15315878
Language	English
Publisher	Springer US
Publisher Date	2016-08-03
Publisher Place	New York
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Active noise control Least mean kurtosis algorithm Recursive sampled variance Substation noise control Circuits and Systems Electrical Engineering Signal,Image and Speech Processing Electronics and Microelectronics, Instrumentation
Content Type	Text
Resource Type	Article
Subject	Applied Mathematics Signal Processing

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