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      • Dept. of Electrical and Electronics Engineering - Master's degree
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      •   BUIR Home
      • University Library
      • Bilkent Theses
      • Theses - Department of Electrical and Electronics Engineering
      • Dept. of Electrical and Electronics Engineering - Master's degree
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      Automatic radar antenna scan analysis in electronic warfare

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      Author(s)
      Eravcı, Bahaeddin
      Advisor
      Barshan, Billur
      Date
      2010
      Publisher
      Bilkent University
      Language
      English
      Type
      Thesis
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      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.
      Keywords
      electronic warfare signal processing
      support vector machines
      artificial neural networks
      decision trees
      naive Bayes classifiers
      pattern recognition
      antenna scan analysis
      antenna scanperiod estimation
      antenna scan type
      Permalink
      http://hdl.handle.net/11693/15133
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      • Dept. of Electrical and Electronics Engineering - Master's degree 655
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