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http://dx.doi.org/10.12673/jant.2022.26.3.179

A study on the Generation Method of Aircraft Wing Flexure Data Using Generative Adversarial Networks  

Ryu, Kyung-Don (The 1st R&D Institute, Agency for Defense Development)
Publication Information
Journal of Advanced Navigation Technology / v.26, no.3, 2022 , pp. 179-184 More about this Journal
Abstract
The accurate wing flexure model is required to improve the transfer alignment performance of guided weapon system mounted on a wing of fighter aircraft or armed helicopter. In order to solve this problem, mechanical or stochastical modeling methods have been studying, but modeling accuracy is too low to be applied to weapon systems. The deep learning techniques that have been studying recently are suitable for nonlinear. However, operating fighter aircraft for deep-learning modeling to secure a large amount of data is practically difficult. In this paper, it was used to generate amount of flexure data samples that are similar to the actual flexure data. And it was confirmed that generated data is similar to the actual data by utilizing "measures of similarity" which measures how much alike the two data objects are.
Keywords
Generative Adversarial Network; Inertial navigation; Transfer alignment; Wing flexure;
Citations & Related Records
Times Cited By KSCI : 1  (Citation Analysis)
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