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2D Millimeter-Wave SAR Imaging with Automotive Radar
(2024) Yalçınkaya, Bengisu; Aydın, Elif; Çoruk, Remziye Büşra; Kara, Ali
In recent years, millimeter wave (mmWave) frequency modulated continuous wave (FMCW) radars have gained popularity in radar imaging applications, coinciding with the advancement of mmWave technology. However, high cost and integration complexity still remain as issues in cases where the target aperture is large. This work introduces a low-cost, low-complexity, and easy-to-implement two-dimensional (2D) mmWave synthetic aperture radar (SAR) system. A commercial-off-the-shelf (COTS) mmWave FMCW radar sensor operating in the frequency range of 77-81 GHz is employed. A large mechanical scanning system which can move in both vertical and horizontal directions is constructed and integrated with the radar sensor. Developing a graphical user interface (GUI), an automatic movement of the scanner is achieved. Experimental measurements are taken in a laboratory environment and the effectiveness of the system is demonstrated. A cross-shaped metal object and a drone are chosen as targets and the SAR images of targets are obtained. For simplicity, by employing a single transceiver pair, sparse samples are taken in a large scanning aperture. It has been shown that the proposed scanning system has great potential in SAR imaging of large objects such as unmanned aerial vehicles (UAV).
Drone Swarm Classification from ISAR Imaging
(2024) Çoruk, Remziye Büşra; Kara, Ali; Aydın, Elif
In today's technology, the use of drones has become very popular for they can be easily purchased over the Internet and can be easily developed. With drones that have wide usage areas, swarm structures have become popular. However, this has brought about some problems. The issue of drone detection has emerged in order to prevent the uncontrolled use of drone swarms in the airspace. Drone swarm detection is important to prevent dangerous accidents or criminal acts. In this study, a new classification algorithm is proposed with deep learning using inverse synthetic aperture radar (ISAR) images of drone swarms based on various formation swarm types. ISAR images are created using ANSYS simulation. Additionally, high frequency structural simulator (HFSS) - shooting bouncing ray (SBR+) solver is used for high-speed computation. Radar and simulation parameters to obtain ISAR images are discussed. Especially, down-range and cross-range resolution parameters are taken into account to achieve high resolution. ISAR images are classified using deep learning methods in terms of formation. Formation types include Line, Square, Cross, and Triangle. The convolutional neural network (CNN) model is used to solve classification problems. The model consists of train, validation, and test steps. Classification performance results are presented with high accuracy. The developed method can be used for anti-drone technologies.
Evaluating the Educational Effectiveness of Radar Systems Laboratory Sessions in the Undergraduate Curriculum
(2024) Yalçınkaya, Bengisu; Benzaghta, Mohamed; Çoruk, Remziye Büşra; Kara, Ali
Introductory courses regarding radar technologies are very popular in the undergraduate curriculum of many electrical and electronics engineering departments. Hands-on experience is an essential part for understanding the theoretical concepts given in lectures. In most cases, it is not affordable for universities to acquire experimental radar systems, especially those in developing countries. This paper presents a detailed description of a cost-effective, easy-to-deploy radar system laboratory sessions and measures the educational effectiveness of the proposed material. The provided radar models can be used in teaching undergraduate students the working principles of frequency modulated continuous wave (FMCW) radar systems, as well as assisting graduate students in their research activities. The effectiveness of the laboratory sessions is measured thoroughly via qualitative and quantitative methods based on the proposed learning process and students’ success. The results show that the lab sessions have increased the students' understanding of the topics covered within the course, and the students' general perception is positive.
Hierarchical Classification of Analog and Digital Modulation Schemes Using Higher-Order Statistics and Support Vector Machines
(2022) Yalçınkaya, Bengisu; Çoruk, Remziye Büşra; Kara, Ali; Tora, Hakan
Mmdalga Radar Kullanarak Drone Sürülerinin ISAR Görüntülenmesi
(2025) Çoruk, Remziye Büşra; Aydın, Elif; Kara, Ali
Teknolojide drone ve drone sürülerinin kullanımının artmasıyla birlikte, anti-drone teknolojilerinin kullanımı önemli ölçüde artmıştır. Ancak, sınırlı görüş alanına sahip senaryolarda drone ve drone sürülerinin tespiti literatürde kalıcı bir zorluk olmaya devam etmektedir. Bu tez, milimetre dalga (mmWave) frekans bantlarında yeniden oluşturulan drone sürülerinin Ters Sentetik Açıklıklı Radar (ISAR) görüntülerinin, oluşumlarına, boyutlarına ve yük yapılandırmalarına göre sınıflandırılmasına odaklanmaktadır. Drone sürülerinin ISAR görüntüleri, ANSYS Yüksek Frekanslı Yapısal Simülatör (HFSS) elektromanyetik simülasyon yazılımı kullanılarak üretilmiştir. Sürü yapıları, quadcopter dronlar kullanılarak modellenmiş ve oluşum tipleri, çizgi, çarpı, kare ve üçgen gibi temel geometrik şekillerle tasarlanmıştır. Sürülerdeki dronlar, orta, küçük ve mini olmak üzere üç boyutta kategorize edilmiştir. Ek olarak, yük dronları sürü yapılandırmalarına dahil edilmiştir. Yüksek çözünürlüklü ISAR görüntüleri elde etmek için radar ve simülasyon parametreleri optimize edilmiştir. Veri setini genişletmek için, ISAR görüntüleri çeşitli bakış açılarında (0° ila 350° arasında 10° artışlarla) oluşturulmuştur. ISAR görüntüleri kullanılarak sürü oluşumu tiplerinin belirlenmesi, görüntü tanıma aşamasında bir Evrişimsel Sinir Ağı (CNN) aracılığıyla gerçekleştirildi. Bunu takiben, nesne algılama aşamasında Sadece Bir Kez Bak (YOLO) algoritması kullanılarak drone boyutu ve yük tespiti gerçekleştirildi. Bu tezde elde edilen sonuçlar oldukça ümit vericidir. Genişletilmiş bir veri seti ve tespit algoritması sunarak, bu çalışma literatüre önemli katkıda bulunmaktadır.
On the Classification of Modulation Schemes Using Higher Order Statistics and Support Vector Machines
(2021) Yalçınkaya, Bengisu; Benzaghta, Mohamed; Çoruk, Remziye Büşra; Kara, Ali
Abstract The recognition of modulation schemes in military and civilian applications is a major task for intelligent receiving systems. Having no previous knowledge of the transmitted signal as well as uncertainties in the channel and the receiver makes the identification of the modulation scheme a difficult task. Various Automatic Modulation Classification (AMC) algorithms have been developed to overcome this challenging task. However, classification with low computational complexity as well as reasonable processing time is still a challenge, especially, for modulation types with similar constellations under realistic channel conditions. In this paper, a feature-based approach along with various classifiers is employed based on statistical features as well as higher-order moments and cumulants. First, some well-known classifiers including Decision Trees (DT), Support Vector Machines (SVM), K-Nearest Neighbors (KNN) and ensemble are evaluated at different SNR values. Then, various forms of SVMs have been utilized. An over-the-air (OTA) recorded dataset consisting of four analog and ten digital modulation schemes are used for testing the proposed method. The classification performance is compared against SNR (0-20 dB). The overall accuracy for quadratic SVM is found to be as high as 98% at 10 dB. This has been comparable with the reported performance based on simulated dataset. Overall, the comparison of the results with those published in the literature indicates that this is the first paper presenting such a high accuracy with OTA dataset consisting of fourteen modulation schemes.
Un Enfoque de Aula Invertida Híbrida: Evaluación de La Opinión y El Rendimiento de Los Estudiantes
(2023) Benzaghta, Mohamed; Çoruk, Remziye Büşra; Gokdogan, Bengisu Yalcinkaya; Kara, Ali
This study presents an improved hybrid-flipped classroom (hybrid-FC) education method based on technology-enhanced learning (TEL) along with diluted classes for a course on probability and random processes in engineering. The proposed system was implemented with the participation of two student groups who alternated weekly between attending face-to-face activities and fully online classes as a sanitary measure during the pandemic. The education model was combined with the flipped classroom (FC) approach in order to improve the quality of learning and address the negative effects of remote education. Before the lessons, the students studied the course material, filled a question form, and then took a low-stake online quiz. Then, the students attended a session where the questions reported in the forms were discussed, and they took an online problem-solving session followed by an individual quiz. Class sessions were available to both online and face-to-face students, as well as in the form of video recordings for anyone who missed lessons. Qualitatively and quantitatively, the proposed education method proved to be more effective and comprehensive than conventional online methodologies. The students' performances were evaluated via quizzes and exams measuring the achievement of the course learning outcomes (CLOs). Weekly pre/post-tests were applied to examine the students’ progress in each topic. Midterm and final exams were planned to measure the level of success for all course topics. Additionally, the students' perception was assessed with questionnaires and face-to-face interviews. A performance assessment showed an apparent increase in the success rate, and the students' perception was found to be positive.