Ahmet Güngör Pakfiliz

Cognitive electronic warfare (CEW) will soon replace conventional electronic warfare (EW) systems in the operational area. Although the CEW concept is utilized on various platforms, it has primarily emerged in military air platforms. Radar warning receiver (RWR) applications are essential in the CEW concept. Effective RWR systems accurately identify radar emitters in complex and dynamic environments, which is critical for situational awareness of air vehicle systems. systems provide information to countermeasure systems, increasing operational success by neutralizing enemy radar and defense systems. To sense the threats and apply appropriate countermeasures, such as jamming or dispensing chaff, requires a quick response and accurate RWR processes. RWR processes are summarized as receiving, signal detection, deinterleaving, labeling, and emitter identification. This study investigates the application of machine learning (ML) techniques to the emitter identification process. The research demonstrates significant improvements in emitter identification accuracy and distance determination by using ML algorithms. The findings suggest that CEW strategies incorporating adaptive learning models can enhance RWR system performance in identifying and categorizing emitters, even in dense Radio frequency (RF) environments. In the trials, it is observed that while the decision tree, ensemble bagged, and support vector machines achieved 100% success in the classification step, the Gaussian method was the most successful, with a mean absolute percent error of 1.6 in the distance calculation step using regression. These results suggest a promising direction for developing more intelligent and resilient EW systems. Cite this article as: İ. Çetin Taş and A. G. Pakfiliz, “Cognitive electronic warfare application to emitter identification process in airborne radar warning receiver systems,” Electrica, 2025, 25, 0182, doi: 10.5152/electrica.2025.24182.

Radar warning receivers (RWRs) are critical for swiftly and accurately identifying potential threats in complex electromagnetic environments. Numerous methods have been developed over the years, with recent advances in artificial intelligence (AI) significantly enhancing RWR capabilities. This study presents a machine learning-based approach for emitter identification within RWR systems, leveraging a comprehensive radar signal library. Key parameters such as signal frequency, pulse width, pulse repetition frequency (PRF), and beam width were extracted from pulsed radar signals and utilized in various machine learning algorithms. The preprogramming phase of RWRs was optimized through the application of multiple classification algorithms, including k-Nearest Neighbors (KNN), Decision Tree (DT), the ensemble learning method, support vector machine (SVM), and Artificial Neural Network (ANN). These algorithms were compared against conventional methods to evaluate their performance. The machine learning models demonstrated a high degree of accuracy, achieving over 95% in training phases and exceeding 99% in test simulations. The findings highlight the superiority of machine learning algorithms in terms of speed and precision when compared to traditional approaches. Furthermore, the flexibility of machine learning techniques to adapt to diverse problem sets underscores their potential as a preferred solution for future RWR applications. This study suggests that the integration of machine learning into RWR emitter identification not only enhances the operational efficiency of electronic warfare (EW) systems but also represents a significant advancement in the field. The increasing relevance of machine learning in recent years positions it as a promising tool for addressing complex signal processing challenges in EW.

This paper proposes a comprehensive power system designed for the use of a more electric aircraft power distribution system. Instead of traditional Nicad battery solutions as the energy source of the aircraft power system, lithium battery structures, which are a recent and promising solution in the field of aviation power systems, are modeled and analyzed. In this study, a WPWM-based, single-phase, multi-level pure sine wave static aircraft-inverter system is designed and integrated to improve the performance of conventional aircraft power systems. In the designed power system, a boost converter structure is proposed that boosts 28 VDC-to-270 VDC voltage coming from the lithium–ion battery pack and can reach a steady state in 0.032 s. The performance of the modeled WPWM-based aircraft-inverter system, compared to SPWM Bipolar and Unipolar switching techniques commonly used in single-phase inverter designs, reveals a THD reduction of approximately 27% with WPWM, resulting in a THD value below 2% for both load current and load voltage. As a result of the study, a power system that will enable the aircraft avionics, ventilation, and navigation systems to perform better than conventional power systems and comply with aircraft electric-power characteristic standards has been designed and detailed.

Decoys have a crucial role in the electronic countermeasure and deception concepts, and many works and developments have been realized over the past few decades. The most impressive one is the miniature air-launched decoy (MALD)s. They are used to give the impression of an attack in enemy airspace by imitating the fighters' radar signatures and flight profiles. However, they resemble the emission of the fighters; their radar detection probabilities are lower than fighters due to their low radar cross-section (RCS)s. This situation results suspiciously and provides a basis for discriminating a MALD from a fighter. Thus, the emission and the MALD system's RCS must resemble a fighter. This study presents a method for increasing the RCS of MALD systems by using phased array antennas. So, a different approach is developed for covering the surface of the MALD platform with a phased array antenna with using ANSYS High Frequency Structural Simulator (HFSS) program. The phased array antenna structure analysis for different radiating elements has been conducted on the ANSYS HFSS program. A microstrip antenna, a dipole antenna, and bow tie antenna structures were analyzed, and the results were compared. The antenna structure with the best results was decided and applied to the missile. The increased MALD system's RCS has been compared with the fighter's RCS, and satisfactory results have been obtained.

Pamukkale University Journal of Engineering Sciences

Improvised explosive devices (IEDs) have recently become a considerable national security concern for governments due to their increased use. Since IEDs do not have a particular shape, detecting and classifying IEDs becomes a complex problem. Due to the irregularity of the wire shape, recent research focuses on detecting command wire, which is the triggering mechanism in IEDs. This study proposes a detection and clustering algorithm for wire detection. The synthetic data were generated using the gprMax software, an open-source Finite-Difference Time-Domain (FDTD) simulation environment. Different wire and clutter orientations were simulated while creating a 2D GPR database. Three different prescreening algorithms are compared concerning computational time and signal-to-noise ratio. The Go Decomposition (GoDec) method was used at the preprocessing stage. Discriminating the buried wire from clutter was conducted using the k-means clustering method. The proposed algorithm results show promising outcomes over simulated GPR 2D C-scans.