Design of self-organizing map type electromagnetic target classifiers for dielectric spheres and conducting aircraft targets with investigation of their noise performances

Date

2009

Author

Katılmış, Tufan Taylan

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The Self-Organizing Map (SOM) is a type of neural network that forms a regular grid of neurons where clusters of neurons represent different classes of targets. The aim of this thesis is to design electromagnetic target classifiers by using the Self-Organizing Map (SOM) type artificial neural networks for dielectric and conducting objects with simple or complex geometries. Design simulations will be realized for perfect dielectric spheres and also for small-scaled aircraft targets modeled by thin conducting wires. The SOM classifiers will be designed by target features extracted from the scattered signals of targets at various aspects by using the Wigner distribution. Noise performance of classifiers will be improved by using slightly noisy input data in SOM training.

Subject Keywords

Electrical engineering., Electronics.

URI

http://etd.lib.metu.edu.tr/upload/12611250/index.pdf
https://hdl.handle.net/11511/19084

Collections

Graduate School of Natural and Applied Sciences, Thesis