Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/143203
Title: Optimal leak factor selection for the output-constrained leaky filtered-input least mean square algorithm
Authors: Shi, Dongyuan
Lam, Bhan
Gan, Woon-Seng
Wen, Shulin
Keywords: Engineering::Electrical and electronic engineering
Issue Date: 2019
Source: Shi, D., Lam, B., Gan, W.-S., & Wen, S. (2019). Optimal leak factor selection for the output-constrained leaky filtered-input least mean square algorithm. IEEE Signal Processing Letters, 26(5), 670-674. doi:10.1109/lsp.2019.2903908
Journal: IEEE Signal Processing Letters
Abstract: The leaky filtered-input least mean square (LFxLMS) algorithm is widely used in active noise control applications to minimize the degradation of attenuation performance due to output saturation distortion. However, the leak factor, which is critical in determining the steady-state error and robustness of the algorithm, is usually selected through trial and error. This letter proposes a leak factor selection approach, which ensures the LFxLMS algorithm converges to its optimal solution under the average-output-power constraint and can be readily derived in practice. Both broadband and narrowband cases are considered in the derivation without the independence assumption, and the simulations are conducted based on real primary and secondary paths to verify its effectiveness.
URI: https://hdl.handle.net/10356/143203
ISSN: 1070-9908
DOI: 10.1109/lsp.2019.2903908
Rights: © 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. The published version is available at: https://doi.org/10.1109/lsp.2019.2903908
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:EEE Journal Articles

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