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

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dc.authoridMenguc, Engin Cemal/0000-0002-0619-549X
dc.contributor.authorMenguc, Engin Cemal
dc.contributor.authorAcir, Nurettin
dc.date.accessioned2024-11-07T13:31:55Z
dc.date.available2024-11-07T13:31:55Z
dc.date.issued2017
dc.departmentNiğde Ömer Halisdemir Üniversitesi
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.
dc.identifier.doi10.1016/j.sigpro.2017.01.031
dc.identifier.endpage21
dc.identifier.issn0165-1684
dc.identifier.issn1872-7557
dc.identifier.scopus2-s2.0-85011665438
dc.identifier.scopusqualityQ1
dc.identifier.startpage10
dc.identifier.urihttps://doi.org/10.1016/j.sigpro.2017.01.031
dc.identifier.urihttps://hdl.handle.net/11480/15123
dc.identifier.volume137
dc.identifier.wosWOS:000398752900002
dc.identifier.wosqualityQ1
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherElsevier
dc.relation.ispartofSignal Processing
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzKA_20241106
dc.subjectAugmented statistics
dc.subjectComplex-valued adaptive filter
dc.subjectCircular signals
dc.subjectNoncircular signals
dc.subjectLyapunov stability theory
dc.titleAn augmented complex-valued Lyapunov stability theory based adaptive filter algorithm
dc.typeArticle

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