Atmospheric Refractivity Estimation from Radar Sea Clutter Using Novel Hybrid Model of Genetic Algorithm and Artificial Neural Networks
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Date
2020-09
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Mark
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Společnost pro radioelektronické inženýrství
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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.
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Radioengineering. 2020 vol. 29, č. 3, s. 512-520. ISSN 1210-2512
https://www.radioeng.cz/fulltexts/2020/20_03_0512_0520.pdf
https://www.radioeng.cz/fulltexts/2020/20_03_0512_0520.pdf
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Peer-reviewed
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en