2 results
Search Results
Now showing 1 - 2 of 2
Article Low Signature UAVs: Radar Cross Section Analysis, Simulation, and Measurement in X-Band(Springer London Ltd, 2025) Unalir, Dizdar; Yalcinkaya, Bengisu; Aydin, ElifThe increasing prevalence of unmanned aerial vehicles (UAVs) is driving the development of radar systems capable of detecting them. This hampers the deployment of UAVs in military operations. While radar cross section reduction (RCSR) can be a valuable solution, the research on this subject is inadequate. This paper presents an RCSR approach adopting a shaping technique for UAVs, demonstrating the proposed approach's efficacy through simulations and actual experimental measurements performed in X-Band on a four-legged UAV model. Using electromagnetic computational instruments, the shaping is applied to the designed UAV model with parameter-based simulations, the simulated radar cross section (RCS) values are derived, and the comparative analysis of these instruments is conducted. Experimental measurements are performed in laboratory conditions using a vector network analyzer. Actual measurement results are validated by simulative findings with the examination of the influence of frequency, polarization, and aspect angle on RCS. The demonstrated measuring approach allows cost-effective and easily applicable research on RCS in X-Band, a commonly utilized frequency range in military. An average RCSR of 10 dBsm has been accomplished with the presented shaping approach.Article Citation - WoS: 5Citation - Scopus: 5Hierarchical Classification of Analog and Digital Modulation Schemes Using Higher-Order Statistics and Support Vector Machines(Springer, 2024) Yalcinkaya, Bengisu; Coruk, Remziye Busra; Kara, Ali; Tora, HakanAutomatic modulation classification (AMC) algorithms are crucial for various military and commercial applications. There have been numerous AMC algorithms reported in the literature, most of which focus on synthetic signals with a limited number of modulation types having distinctive constellations. The efficient classification of high-order modulation schemes under real propagation effects using models with low complexity still remains difficult. In this paper, employing quadratic SVM, a feature-based hierarchical classification method is proposed to accurately classify especially higher-order modulation schemes and its performance is investigated using over the air (OTA) collected data. Statistical features, higher-order moments, and higher-order cumulants are utilized as features. Then, the performances of some well-known classifiers are evaluated, and the classifier presenting the best performance is employed in the proposed hierarchical classification model. An OTA dataset containing 17 analog and digital modulation schemes is used to assess the performance of the proposed classification model. With the proposed hierarchical classification algorithm, a significant improvement has been achieved, especially in higher-order modulation schemes. The overall accuracy with the proposed hierarchical structure is 96% after 5 dB signal-to-noise ratio value, approximately a 10% increase is achieved compared to the traditional classification algorithm.
