NEURAL NETWORK CLASSIFICATION METHOD FOR AIRCRAFT IN ISAR IMAGES

Authors

  • Krasimir Ognyanov Slavyanov Department Computer Systems and Technologies, “Vasil Levski” National Military University, Shumen

DOI:

https://doi.org/10.17770/etr2019vol2.4074

Keywords:

artificial neural network, engine position, reference model

Abstract

This article offers a neural network method for automatic classification of Inverse Synthetic Aperture Radar objects represented in images with high level of post-receive optimization. A full explanation of the procedures of two-layer neural network architecture creating and training is described. The classification in the recognition stage is proposed, based on several main classes or sets of flying objects. The classification sets are designed according to distinctive specifications in the structural models of the aircrafts. The neural network is experimentally simulated in MATLAB environment. Numerical results of the experiments carried, prove the correct classification of the objects in ISAR optimized images.

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Author Biography

  • Krasimir Ognyanov Slavyanov, Department Computer Systems and Technologies, “Vasil Levski” National Military University, Shumen
    KRASIMIR O. SLAVYANOV was born in Krumovgrad, Bulgaria and went to the "Vasil Levski" National MIlitary University, where he studied computer systems and technologies and obtained his degree in 2008. He worked for a couple of years for the Bulgarian Land forces, Ministry of Defence before moving in 2011 to the National Military University where he is now Assist. Prof. in Faculty "Artillery, Air Defence and Communication and Information Systems", department "Computer systems and technologies". He is part of a small team working in the field of development of ISAR and AI technologies. His e-mail address is : k.o.slavyanov@gmail.com.

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Published

2019-06-20

How to Cite

[1]
K. O. Slavyanov, “NEURAL NETWORK CLASSIFICATION METHOD FOR AIRCRAFT IN ISAR IMAGES”, ETR, vol. 2, pp. 141–145, Jun. 2019, doi: 10.17770/etr2019vol2.4074.