An augmented complex-valued Lyapunov stability theory based adaptive filter algorithm

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dc.authorid0000-0003-1186-3058en_US
dc.contributor.authorMenguc, Engin Cemal
dc.contributor.authorAcır, Nurettin
dc.date.accessioned2021-03-20T20:13:46Z
dc.date.available2021-03-20T20:13:46Z
dc.date.issued2017
dc.departmentBTÜ, Mühendislik ve Doğa Bilimleri Fakültesi, Elektrik Elektronik Mühendisliği Bölümüen_US
dc.descriptionMenguc, Engin Cemal/0000-0002-0619-549Xen_US
dc.description.abstractA novel augmented complex-valued Lyapunov stability theory (LST) based adaptive filter (ACLAF) algorithm is proposed for the widely linear adaptive filtering of noncircular complex-valued signals. After a candidate Lyapunov function is determined, the design procedure is formulated as an inequality constrained optimization problem by using augmented statistics and LST. Thus, the proposed algorithm has improved the adaptive filtering of noncircular complex-valued signals by a unified framework of the LST and augmented complex statistics. Moreover, we statistically show that the ACLAF algorithm converges to the optimal Wiener solution under stationary environments, the required condition of the step size for the stability of the ACLAF algorithm is obtained by convergence in mean analysis and a new approach. In addition, the variance of the ACLAF algorithm is statically analysed in this study. The performance of the ACLAF algorithm is tested on circular and noncircular benchmark signals and on a real-world non circular wind signal. Simulation results verify that the ACLAF algorithm outperforms complex-valued LST based adaptive filter (CLAF), complex-valued least mean square (CLMS), complex-valued normalized least mean square (CNLMS), augmented CLMS (ACLMS) and augmented CNLMS (ACNLMS) algorithms for adaptive prediction of noncircular signals in terms of prediction gain, convergence rate and mean square error (MSE). Also, the ACLAF algorithm enhances the prediction gain by more than 25% when compared to the other augmented algorithms. (C) 2017 Elsevier B.V. All rights reserved.en_US
dc.identifier.doi10.1016/j.sigpro.2017.01.031en_US
dc.identifier.endpage21en_US
dc.identifier.issn0165-1684
dc.identifier.issn1872-7557
dc.identifier.scopusqualityQ1en_US
dc.identifier.startpage10en_US
dc.identifier.urihttp://doi.org/10.1016/j.sigpro.2017.01.031
dc.identifier.urihttps://hdl.handle.net/20.500.12885/937
dc.identifier.volume137en_US
dc.identifier.wosWOS:000398752900002en_US
dc.identifier.wosqualityQ1en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.institutionauthorAcır, Nurettin
dc.language.isoenen_US
dc.publisherElsevieren_US
dc.relation.ispartofSignal Processingen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectAugmented statisticsen_US
dc.subjectComplex-valued adaptive filteren_US
dc.subjectCircular signalsen_US
dc.subjectNoncircular signalsen_US
dc.subjectLyapunov stability theoryen_US
dc.titleAn augmented complex-valued Lyapunov stability theory based adaptive filter algorithmen_US
dc.typeArticleen_US

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