Browsing by Author "Bektas, Almila"

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Citation - WoS: 2
Citation - Scopus: 2
Development of Air-To Engagement Analysis Model of Fighter Aircrafts
(Gazi Univ, Fac Engineering Architecture, 2022) Bektas, Almila; Ergezer, Halit; Erdogan, Sinem
In operational analysis studies; it is possible to model and simulate at an engineering level, engagement level, task level and campaign forces level. In this study, modelling and simulation studies are performed in engagement-level allowing the analysis of air-to-ground engagement effectiveness of fighter aircraft according to the operational environment. The operating environment of the combat aircraft, which provides survivability analysis based on low visibility and electronic mixing capabilities, is created. The search radar and tracking radar models for ground-to-air threats have been designed in accordance with the engagement level. The dynamic model of the fighter aircraft and the ground-to-air missile have been modelled using pseudo 5 degree-of-freedom. Modelling has been carried out to allow the use of changes in the Radar Crosssectional Area (RCS), which is one of the most important factors affecting the survivability of the aircraft, with respect to azimuth and elevation angles. The Radio Frequency (RF) jamming capability of the fighter aircraft has also been modelled in accordance with the engagement level. The results of the generic scenarios for the analysis of the effect of these models' parameters on the survivability of fighter aircraft have been presented.
Lpi Radar Waveform Classification Using Binary Svm and Multi-Class Svm Based on Principal Components of Tfi
(2020) Ergezer, Halit; Bektas, Almila
Since cognition has become an important topic in Electronic Warfare(EW) systems, Electronic Support Measures (ESM) are used to monitor, interceptand analyze radar signals. Low Probability of Intercept (LPI) radars are preferredto be able to detect targets without being detected by ESM systems. Because of theirproperties as low power, variable frequency, wide bandwidth, LPI Radarwaveforms are difficult to intercept with ESM systems. In addition to intercepting,the determination of the waveform types used by the LPI Radars is also veryimportant for applying counter-measures against these radars. In this study, asolution for the LPI Radar waveform recognition is proposed. The solution is basedon the training of Support Vector Machine (SVM) after applying PrincipalComponent Analysis (PCA) to the data obtained by Time-Frequency Images (TFI).TFIs are generated using Choi-Williams Distribution. High energy regions on theseimages are cropped automatically and then resized to obtain uniform data set. Toobtain the best result in SVM, the SVM Hyper-Parameters are also optimized.Results are obtained by using one-against-all and one-against-one methods. Betterclassification performance than those given in the literature has been obtainedespecially for lower Signal to Noise Ratio (SNR) values. The cross-validated resultsobtained are compared with the best results in the literature.