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

Download

index.pdf

Date

2009

Author

Katılmış, Tufan Taylan

Metadata

Show full item record

Item Usage Stats

240
views

90
downloads

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

Suggestions

OpenMETU
Core

Computation of radar cross sections of complex targets by shooting and bouncing ray method Özgün, Salim; Kuzuoğlu, Mustafa; Department of Electrical and Electronics Engineering (2009) In this study, a MATLAB® code based on the Shooting and Bouncing Ray (SBR) algorithm is developed to compute the Radar Cross Section (RCS) of complex targets. SBR is based on ray tracing and combine Geometric Optics (GO) and Physical Optics (PO) approaches to compute the RCS of arbitrary scatterers. The presented algorithm is examined in two parts; the first part addresses a new aperture selection strategy named as “conformal aperture”, which is proposed and formulated to increase the performance of the cod...
A comparative performance evaluation of scale invariant interest point detectors for infrared and visual images Emir, Erdem; Alatan, Abdullah Aydın; Department of Electrical and Electronics Engineering (2008) In this thesis, the performance of four state-of-the-art feature detectors along with SIFT and SURF descriptors in matching object features of mid-wave infrared, long-wave infrared and visual-band images is evaluated across viewpoints and changing distance conditions. The utilized feature detectors are Scale Invariant Feature Transform (SIFT), multiscale Harris-Laplace, multiscale Hessian-Laplace and Speeded Up Robust Features (SURF) detectors, all of which are invariant to image scale and rotation. Feature...
Investigation of music algorithm based and wd-pca method based electromagnetic target classification techniques for their noise performances Ergin, Emre; Sayan, Gönül; Department of Electrical and Electronics Engineering (2009) Multiple Signal Classification (MUSIC) Algorithm based and Wigner Distribution-Principal Component Analysis (WD-PCA) based classification techniques are very recently suggested resonance region approaches for electromagnetic target classification. In this thesis, performances of these two techniques will be compared concerning their robustness for noise and their capacity to handle large number of candidate targets. In this context, classifier design simulations will be demonstrated for target libraries con...
Parallel implementation of the boundary element method for electromagnetic source imaging of the human brain Ataseven, Yoldaş; Gençer, Nevzat Güneri; Department of Electrical and Electronics Engineering (2005) Human brain functions are based on the electrochemical activity and interaction of the neurons constituting the brain. Some brain diseases are characterized by abnormalities of this activity. Detection of the location and orientation of this electrical activity is called electro-magnetic source imaging (EMSI) and is of signi cant importance since it promises to serve as a powerful tool for neuroscience. Boundary Element Method (BEM) is a method applicable for EMSI on realistic head geometries that generates...
Real time 3d surface feature extraction on fpga Tellioğlu, Zafer Haşim; Ulusoy, İlkay; Department of Electrical and Electronics Engineering (2010) Three dimensional (3D) surface feature extractions based on mean (H) and Gaussian (K) curvature analysis of range maps, also known as depth maps, is an important tool for machine vision applications such as object detection, registration and recognition. Mean and Gaussian curvature calculation algorithms have already been implemented and examined as software. In this thesis, hardware based digital curvature processors are designed. Two types of real time surface feature extraction and classification hardwar...

Citation Formats

T. T. Katılmış, “Design of self-organizing map type electromagnetic target classifiers for dielectric spheres and conducting aircraft targets with investigation of their noise performances,” M.S. - Master of Science, Middle East Technical University, 2009.