Novel error variance estimation rule for nonparametric VSS-NLMS algorithm

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dc.contributor.authorMenguc, Engin Cemal
dc.date.accessioned2024-11-07T13:25:00Z
dc.date.available2024-11-07T13:25:00Z
dc.date.issued2020
dc.departmentNiğde Ömer Halisdemir Üniversitesi
dc.description.abstractThis paper presents a robust error variance estimation rule for the nonparametric variable step-size normalized least mean square (NPVSS-NLMS) algorithm. The proposed variance estimation rule accurately estimates the variance of the error signal. This is achieved by the variable exponential windowing parameter depending on the standard deviations of the sequential error signals. The accurate estimation of the error signal variance in the NPVSS-NLMS algorithm considerably improves the performance of the adaptive filter when compared to the classical NPVSS-NLMS algorithm. Moreover, the convergence and steady-state performances of the NPVSS-NLMS based on the proposed rule are analyzed in this study. The performance of the proposed algorithm is evaluated on system identification and acoustic echo canceling experiments and compared with that the classical NPVSS-NLMS algorithm. As a result, simulations show that the proposed algorithm with the help of the novel robust error variance estimation rule not only yields a dramatically reduced steady-state error but also achieves a faster convergence rate as compared with the classical counterparts. Furthermore, the theoretical results of the variable exponential windowing parameter used in the proposed rule are in very good agreement with its simulation results.
dc.identifier.doi10.1007/s11760-020-01691-7
dc.identifier.endpage1429
dc.identifier.issn1863-1703
dc.identifier.issn1863-1711
dc.identifier.issue7
dc.identifier.scopus2-s2.0-85083837478
dc.identifier.scopusqualityQ2
dc.identifier.startpage1421
dc.identifier.urihttps://doi.org/10.1007/s11760-020-01691-7
dc.identifier.urihttps://hdl.handle.net/11480/14450
dc.identifier.volume14
dc.identifier.wosWOS:000558416100001
dc.identifier.wosqualityQ3
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherSpringer London Ltd
dc.relation.ispartofSignal Image and Video Processing
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzKA_20241106
dc.subjectNormalized least mean square (NLMS)
dc.subjectAdaptive filters
dc.subjectnonparametric variable step size
dc.subjectError variance estimation
dc.titleNovel error variance estimation rule for nonparametric VSS-NLMS algorithm
dc.typeArticle

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