Atmospheric Refractivity Estimation from Radar Sea Clutter Using Novel Hybrid Model of Genetic Algorithm and Artificial Neural Networks
dc.contributor.author | Tepecik, Cemil | |
dc.contributor.author | Navruz, Isa | |
dc.contributor.author | Altinoz, O. Tolga | |
dc.coverage.issue | 3 | cs |
dc.coverage.volume | 29 | cs |
dc.date.accessioned | 2020-10-14T07:07:55Z | |
dc.date.available | 2020-10-14T07:07:55Z | |
dc.date.issued | 2020-09 | cs |
dc.description.abstract | This 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.format | text | cs |
dc.format.extent | 512-520 | cs |
dc.format.mimetype | application/pdf | en |
dc.identifier.citation | Radioengineering. 2020 vol. 29, č. 3, s. 512-520. ISSN 1210-2512 | cs |
dc.identifier.doi | 10.13164/re.2020.0512 | en |
dc.identifier.issn | 1210-2512 | |
dc.identifier.uri | http://hdl.handle.net/11012/195212 | |
dc.language.iso | en | cs |
dc.publisher | Společnost pro radioelektronické inženýrství | cs |
dc.relation.ispartof | Radioengineering | cs |
dc.relation.uri | https://www.radioeng.cz/fulltexts/2020/20_03_0512_0520.pdf | cs |
dc.rights | Creative Commons Attribution 4.0 International license | en |
dc.rights.access | openAccess | en |
dc.rights.uri | http://creativecommons.org/licenses/by/4.0/ | en |
dc.subject | Hybrid intelligent systems | en |
dc.subject | radio wave propagation | en |
dc.subject | surface based duct | en |
dc.subject | parameter estimation | en |
dc.title | Atmospheric Refractivity Estimation from Radar Sea Clutter Using Novel Hybrid Model of Genetic Algorithm and Artificial Neural Networks | en |
dc.type.driver | article | en |
dc.type.status | Peer-reviewed | en |
dc.type.version | publishedVersion | en |
eprints.affiliatedInstitution.faculty | Fakulta eletrotechniky a komunikačních technologií | cs |
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