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dc.contributor.authorTepecik, C.
dc.contributor.authorNavruz, I.
dc.contributor.authorAltinoz, O. T.
dc.date.accessioned2020-10-14T07:07:55Z
dc.date.available2020-10-14T07:07:55Z
dc.date.issued2020-09cs
dc.identifier.citationRadioengineering. 2020 vol. 29, č. 3, s. 512-520. ISSN 1210-2512cs
dc.identifier.issn1210-2512
dc.identifier.urihttp://hdl.handle.net/11012/195212
dc.description.abstractThis paper is focused on solving the inversion problem of refractivity from clutter (RFC) technique. A novel hybrid model is developed that can estimate the atmospheric refractivity (M profile) with a high accuracy, for surface based duct case, which is most effective non¬standard propagation condition on radar observation. The model uses propagation factor curve in horizontal axis, whose characteristics is determined by M profile for esti¬mation. The model is based on artificial neural network, which includes a dynamic training data approach, and a problem adapted genetic algorithm. Dynamic training data set application is a nonstandard approach in neural network applications, in which every obtained result are dynamically added to data set during the estimation pro¬cess, for a better estimation. Firstly, neural network and genetic algorithm have been adapted to the characteristics of inversion problem separately. Then, the mentioned two methods have been harmonized and run together. Ulti-mately, the final algorithm has evolved into a complex adapted hybrid model, which is easily applicable to clutter data obtained by any real radar from the real environment. The results show that the proposed model presents consid¬erably effective solution to refractivity estimation problem.en
dc.formattextcs
dc.format.extent512-520cs
dc.format.mimetypeapplication/pdfen
dc.language.isoencs
dc.publisherSpolečnost pro radioelektronické inženýrstvícs
dc.relation.ispartofRadioengineeringcs
dc.relation.urihttps://www.radioeng.cz/fulltexts/2020/20_03_0512_0520.pdfcs
dc.rightsCreative Commons Attribution 4.0 International licenseen
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/en
dc.subjectHybrid intelligent systemsen
dc.subjectradio wave propagationen
dc.subjectsurface based ducten
dc.subjectparameter estimationen
dc.titleAtmospheric Refractivity Estimation from Radar Sea Clutter Using Novel Hybrid Model of Genetic Algorithm and Artificial Neural Networksen
eprints.affiliatedInstitution.facultyFakulta eletrotechniky a komunikačních technologiícs
dc.coverage.issue3cs
dc.coverage.volume29cs
dc.identifier.doi10.13164/re.2020.0512en
dc.rights.accessopenAccessen
dc.type.driverarticleen
dc.type.statusPeer-revieweden
dc.type.versionpublishedVersionen


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Creative Commons Attribution 4.0 International license
Except where otherwise noted, this item's license is described as Creative Commons Attribution 4.0 International license