TY - JOUR
T1 - Projection FxLMS framework of active noise control against impulsive noise environments
AU - Feng, Pengxing
AU - Wang, Zhi-yong
AU - So, Hing Cheung
PY - 2024/12
Y1 - 2024/12
N2 - This paper focuses on active noise control in the presence of disturbance from an α-stable distribution. The filtered-x fractional lower-order covariance algorithm has been introduced to enhance the performance of traditional algorithms in impulsive noise environments. For minimal adjustment of the weight vector, another filtered-x normalized fractional lower-order covariance (FxNFLOC) algorithm is proposed. Furthermore, the projection approach of the filtered-x least mean square (FxLMS) algorithm is explored. Within this framework, a new algorithm called filtered-x least mean logarithmic square (FxLMLS), which employs a logarithmic function, is devised. The convergence conditions of the developed schemes are also derived. Numerical examples demonstrate the effectiveness and superior performance of the FxNFLOC and FxLMLS algorithms in handling impulsive noise over existing competitors in various scenarios. © 2024 Elsevier B.V.
AB - This paper focuses on active noise control in the presence of disturbance from an α-stable distribution. The filtered-x fractional lower-order covariance algorithm has been introduced to enhance the performance of traditional algorithms in impulsive noise environments. For minimal adjustment of the weight vector, another filtered-x normalized fractional lower-order covariance (FxNFLOC) algorithm is proposed. Furthermore, the projection approach of the filtered-x least mean square (FxLMS) algorithm is explored. Within this framework, a new algorithm called filtered-x least mean logarithmic square (FxLMLS), which employs a logarithmic function, is devised. The convergence conditions of the developed schemes are also derived. Numerical examples demonstrate the effectiveness and superior performance of the FxNFLOC and FxLMLS algorithms in handling impulsive noise over existing competitors in various scenarios. © 2024 Elsevier B.V.
KW - Active impulsive noise control
KW - Adaptive filtering
KW - Convergence analysis
KW - Projection framework
KW - α-stable distribution
UR - http://www.scopus.com/inward/record.url?scp=85199496296&partnerID=8YFLogxK
UR - https://www.scopus.com/record/pubmetrics.uri?eid=2-s2.0-85199496296&origin=recordpage
U2 - 10.1016/j.sigpro.2024.109624
DO - 10.1016/j.sigpro.2024.109624
M3 - RGC 21 - Publication in refereed journal
SN - 0165-1684
VL - 225
JO - Signal Processing
JF - Signal Processing
M1 - 109624
ER -