Mekatronik Mühendisliği Bölümü Yayın Koleksiyonu
Permanent URI for this collectionhttps://hdl.handle.net/20.500.12416/255
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Article Citation - WoS: 2Citation - Scopus: 2Development of Air-To Engagement Analysis Model of Fighter Aircrafts(Gazi Univ, Fac Engineering Architecture, 2022) Bektas, Almila; Ergezer, Halit; Erdogan, SinemIn 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.Article Citation - WoS: 1Citation - Scopus: 2Classification of Low Probability of Intercept Radar Waveforms Using Gabor Wavelets(Gazi Univ, Fac Engineering Architecture, 2021) Ergezer, HalitLow Probability of Intercept (LPI Radar) is a class of radar with specific technical characteristics that make it very difficult to intercept with electronic support systems and radar warning receivers. Because of their properties as low power, variable frequency, wide bandwidth, LPI radar waveforms are difficult to intercept by ESM systems. In recent years, studies on the classification of waveforms used by these types of radar have been accelerated. In this study, Time-Frequency Images (TFI) has been obtained from the LPI radars waveforms by using Choi-Williams Distribution method. From these images, feature vectors have been generated using Gabor Wavelet transform. In contrast to many methods in the literature, waveform classification has been performed by directly comparing the feature vectors obtained without using any machine learning method. With the method we propose, classification accuracies were obtained at intervals of 2 dB between -20 dB and 10 dB and performed at reasonable classification accuracy rates up to -8 dB SNR value. Better results than the best reported in the literature were obtained for some signal types. The results obtained for all waveform types are given in comparison with the results of the existing methods in the literature.