Radar anten taramalarının otomatik analizi

Estimation of the radar antenna scan period and recognition of the antenna scantype is usually performed by human operators in the Electronic Warfare (EW)world. In this thesis, we propose a robust algorithm to automatize these two criticalprocesses. The proposed algorithm consists of two main parts: antenna scanperiod estimation and antenna scan type classification. The first part of the algorithminvolves 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 thatthe extracted features are reliable and are solely the result of the different scantypes, since the effect of the different periods in the signal is removed. Four differentfeatures are extracted from the signal vector with an understanding of thephenomena behind the received signals. These features are used to train naiveBayes classifiers, decision-tree classifiers, artificial neural networks, and supportvector machines. We have developed an Antenna Scan Pattern Simulator (ASPS)that simulates the position of the antenna beam with respect to time and generatessynthetic data. These classifiers are trained and tested with the syntheticdata and are compared by their confusion matrices, correct classification rates,robustness to noise, and computational complexity. The effect of the value of Nand different signal-to-noise ratios (SNRs) on correct classification performanceis investigated for each classifier. Decision-tree classifier is found to be the mostsuitable classifier because of its high classification rate, robustness to noise, andcomputational ease. Real data acquired by ASELSAN Inc. is also used to validatethe algorithm. The results of the real data indicate that the algorithm isready for deployment in the field and is capable of being robust against practicalcomplications.