dc.contributor.advisor | Barshan, Billur | |
dc.contributor.author | Eravcı, Bahaeddin | |
dc.date.accessioned | 2016-01-08T18:13:57Z | |
dc.date.available | 2016-01-08T18:13:57Z | |
dc.date.issued | 2010 | |
dc.identifier.uri | http://hdl.handle.net/11693/15133 | |
dc.description | Ankara : The Department of Electrical and Electronics Engineering and the Institute of Engineering and Sciences of Bilkent University, 2010. | en_US |
dc.description | Thesis (Master's) -- Bilkent University, 2010. | en_US |
dc.description | Includes bibliographical references leaves 63-65. | en_US |
dc.description.abstract | Estimation of the radar antenna scan period and recognition of the antenna scan
type is usually performed by human operators in the Electronic Warfare (EW)
world. In this thesis, we propose a robust algorithm to automatize these two critical
processes. The proposed algorithm consists of two main parts: antenna scan
period estimation and antenna scan type classification. The first part of the algorithm
involves estimating the period of the signal using a time-domain approach.
After this step, the signal is warped to a single vector with predetermined size
(N) by resampling the data according to its period. This process ensures that
the extracted features are reliable and are solely the result of the different scan
types, since the effect of the different periods in the signal is removed. Four different
features are extracted from the signal vector with an understanding of the
phenomena behind the received signals. These features are used to train naive
Bayes classifiers, decision-tree classifiers, artificial neural networks, and support
vector machines. We have developed an Antenna Scan Pattern Simulator (ASPS)
that simulates the position of the antenna beam with respect to time and generates
synthetic data. These classifiers are trained and tested with the synthetic
data and are compared by their confusion matrices, correct classification rates,
robustness to noise, and computational complexity. The effect of the value of N
and different signal-to-noise ratios (SNRs) on correct classification performance
is investigated for each classifier. Decision-tree classifier is found to be the most
suitable classifier because of its high classification rate, robustness to noise, and
computational ease. Real data acquired by ASELSAN Inc. is also used to validate
the algorithm. The results of the real data indicate that the algorithm is
ready for deployment in the field and is capable of being robust against practical
complications. | en_US |
dc.description.statementofresponsibility | Eravcı, Bahaeddin | en_US |
dc.format.extent | xi, 65 leaves, illustrations | en_US |
dc.language.iso | English | en_US |
dc.rights | info:eu-repo/semantics/openAccess | en_US |
dc.subject | electronic warfare signal processing | en_US |
dc.subject | support vector machines | en_US |
dc.subject | artificial neural networks | en_US |
dc.subject | decision trees | en_US |
dc.subject | naive Bayes classifiers | en_US |
dc.subject | pattern recognition | en_US |
dc.subject | antenna scan analysis | en_US |
dc.subject | antenna scanperiod estimation | en_US |
dc.subject | antenna scan type | en_US |
dc.subject.lcc | TK7871.6 .E73 2010 | en_US |
dc.subject.lcsh | Antennas (Electronics) | en_US |
dc.subject.lcsh | Radar--Antennas. | en_US |
dc.subject.lcsh | Signal processing. | en_US |
dc.subject.lcsh | Pattern recognition systems. | en_US |
dc.subject.lcsh | Military telecommunication. | en_US |
dc.title | Automatic radar antenna scan analysis in electronic warfare | en_US |
dc.type | Thesis | en_US |
dc.department | Department of Electrical and Electronics Engineering | en_US |
dc.publisher | Bilkent University | en_US |
dc.description.degree | M.S. | en_US |