Show/Hide Menu
Hide/Show Apps
Logout
Türkçe
Türkçe
Search
Search
Login
Login
OpenMETU
OpenMETU
About
About
Open Science Policy
Open Science Policy
Open Access Guideline
Open Access Guideline
Postgraduate Thesis Guideline
Postgraduate Thesis Guideline
Communities & Collections
Communities & Collections
Help
Help
Frequently Asked Questions
Frequently Asked Questions
Guides
Guides
Thesis submission
Thesis submission
MS without thesis term project submission
MS without thesis term project submission
Publication submission with DOI
Publication submission with DOI
Publication submission
Publication submission
Supporting Information
Supporting Information
General Information
General Information
Copyright, Embargo and License
Copyright, Embargo and License
Contact us
Contact us
Automatic target recognition of quadcopter type drones from moderately-wideband electromagnetic data using convolutional neural networks
Download
index.pdf
Date
2022-12-15
Author
Güneri, Rutkay
Metadata
Show full item record
This work is licensed under a
Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License
.
Item Usage Stats
216
views
169
downloads
Cite This
In this thesis, the classifier design approach based on “Learning by a Convolutional Neural Network (CNN)” will be applied to two different target library/data sets; an ultra-wideband simulation data (from 37 MHz to 19.1 GHz) obtained for a target library of four dielectric spheres, and a moderately-wide band measurement data (from 3.1 to 4.8 GHz) obtained for a target library of four quadcopter type unmanned aerial vehicles (UAVs). While the bandwidth of simulation data for spherical targets is about nine octaves, the bandwidth of measurement data collected for quadcopters is even less than one octave. As the first task, a CNN-based electromagnetic target classifier will be designed for the spherical targets using that spectrally rich simulated database. Then, its performance will be compared to the performance of another classifier that has been already reported in automatic target recognition (ATR) literature as designed by the “Wigner Distribution-Principle Component (WD-PCA) based Feature Extraction” technique using the same target library and the same database. After verifying the effectiveness of the CNN-based classifier design aproach through this comparative investigation, a second CNN-based classifier will be designed for the quadcopter type UAVs using their challenging scattered database of very modest spectral bandwidth. Design details and performances of each classifier will be presented through the thesis discussing the advantages and disadvantages of the CNN-based classifier design approach.
Subject Keywords
Automatic target recognition
,
Electromagnetic target classification
,
Convolutional neural network (CNN)
,
CNN learning for one-dimensional data
,
Quadcopter type UAV classification
URI
https://hdl.handle.net/11511/101331
Collections
Graduate School of Natural and Applied Sciences, Thesis
Suggestions
OpenMETU
Core
Robust Automatic Target Recognition in FLIR imagery
Soyman, Yusuf (2012-04-24)
In this paper, a robust automatic target recognition algorithm in FLIR imagery is proposed. Target is first segmented out from the background using wavelet transform. Segmentation process is accomplished by parametric Gabor wavelet transformation. Invariant features that belong to the target, which is segmented out from the background, are then extracted via moments. Higher-order moments, while providing better quality for identifying the image, are more sensitive to noise. A trade-off study is then perform...
An experimental comparison of symbolic and neural learning algorithms
Baykal, Nazife (1998-04-23)
In this paper comparative strengths and weaknesses of symbolic and neural learning algorithms are analysed. Experiments comparing the new generation symbolic algorithms and neural network algorithms have been performed using twelve large, real-world data sets.
A new neural network approach to the target tracking problem with smart structure
Caylar, Selcuk; Leblebicioğlu, Mehmet Kemal; Dural, Gülbin (2006-12-01)
The algorithm presented in this paper, namely the modified neural multiple source tracking algorithm (MN-MUST) is the modified form of the recently published work, a NN algorithm, the neural multiple-source tracking (N-MUST) algorithm, was presented for locating and tracking angles of arrival from multiple sources. MN-MUST algorithm consists of three stages that are classified as the detection, filtering and DoA estimation stages. In the first stage a number of radial basis function neural networks (RBFNN) ...
Efficient Bayesian track-before-detect
Tekinalp, Serhat; Alatan, Abdullah Aydın (2006-10-11)
This paper presents a novel Bayesian recursive track-before-detect (TBD) algorithm for detection and tracking of dim targets in optical image sequences. The algorithm eliminates the need for storing past observations by recursively incorporating new data acquired through sensor to the existing information. It calculates the likelihood ratio for optimal detection and estimates target state simultaneously. The technique does not require velocity-matched filtering and hence, it is capable of detecting any targ...
A linear approximation for training Recurrent Random Neural Networks
Halıcı, Uğur (1998-01-01)
In this paper, a linear approximation for Gelenbe's Learning Algorithm developed for training Recurrent Random Neural Networks (RRNN) is proposed. Gelenbe's learning algorithm uses gradient descent of a quadratic error function in which the main computational effort is for obtaining the inverse of an n-by-n matrix. In this paper, the inverse of this matrix is approximated with a linear term and the efficiency of the approximated algorithm is examined when RRNN is trained as autoassociative memory.
Citation Formats
IEEE
ACM
APA
CHICAGO
MLA
BibTeX
R. Güneri, “Automatic target recognition of quadcopter type drones from moderately-wideband electromagnetic data using convolutional neural networks,” M.S. - Master of Science, Middle East Technical University, 2022.