Browsing by Author "Ergezer, Halit"

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Çekilebilir Sahte Hedef Etkinliğinin Modelleme Ve Simülasyon İle Analizi
(2019) Özbilge, Kubilay; Ergezer, Halit; 293396; 293396; Mekatronik Mühendisliği
Sahte hedefler, uçağın savaş alanında hayatta kalmasını artırmak için radar güdümlü füzeden korumak için kullanılan bir savunma yöntemlerindendir. Bu çalışmada savaş uçaklarında kullanılan çekilebilir aktif sahte hedeflerin uçağın hayatta kalabilirliğine etkisi, uçağın çekilebilir sahte hedef kullandığı ve kullanmadığı durumlar için vurulma olasılıkları ve füzenin kaçırma uzaklığı değerleri hesaplanarak analiz edilmiştir. Çalışmamızda açık kaynaktan elde edilen veriler kullanılmıştır. Jenerik yapıda kurgulanan savaş uçağı modeli, çekilebilir sahte hedef modeli ve radar modeli ile elde edilen veriler sunulmuştur. Benzetim sonuçları çekilebilir aktif sahte hedef kullanımının savaş uçaklarına özellikle angajmanın son safhasında koruma sağladığını ortaya koymaktadır.
Citation - WoS: 1
Citation - Scopus: 1
Classification of low probability of intercept radar waveforms using gabor wavelets
(Gazi Univ, Fac Engineering Architecture, 2021) Ergezer, Halit; 29339; Mekatronik Mühendisliği
Low 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.
Çoklu ˙IHA Kullanımı ile Kabloya Asılı Yük Ta¸sınımı Transportation of Cable Suspended Slung Load System Using Multiple UAVs
(2022) Can, Süleyman Emre; Leblebicioğlu, M. Kemal; Ergezer, Halit; 293396; Mekatronik Mühendisliği
Bu çalı¸smada, kablolar ile asılı esnemeyen bir yükün ortakla¸sa ta¸sınımı için kontrolcü tasarımı tanıtılmı¸stır. Yük, birden çok insansız hava aracına (˙IHA) yükün kablo ile baglanması ˘ ile ta¸sınmakta olup, istenen üç boyutlu rota üzerinde hareket etmesi saglanmaktadır. Sistemde yük lider olarak alınmaktadır. ˘ Hiyerar¸sik ve merkezcil bir yapıda kontrol edilmektedir. Ta¸sıma görevi, bir optimizasyon problemi olarak ele alınmı¸stır. Sistemi istenen rotada hareket ettirebilmek için bir otopilot algoritması tasarlanmı¸s olup PD yapıları kullanılarak olu¸sturulmu¸stur. Pozisyon kontrolcüsünün çıktısı kontrol atama matrisi tarafından ele alınmaktadır ve uygun kuvvet dagılımını sistemin istenen ˘ davranı¸slarına uygun olacak ¸sekilde türetmektedir. Kontrolcü performansları, takip edilmesi istenen bir rota üzerinde test edilmektedir.
Citation - WoS: 2
Citation - Scopus: 2
Control structure design with constraints for a slung load quadrotor system
(Sage Publications Ltd, 2024) Ergezer, Halit; Leblebicioglu, Kemal; 293396; Mekatronik Mühendisliği
We propose a control structure for a quadrotor carrying a slung load with swing-angle constraints. This quadrotor is supposed to pass through the waypoints at specified speeds. First, a cascaded PID autopilot is designed, which adaptively gives attention to position and speed requirements as a function of their errors. Its parameters are found from an optimization problem solved using the PSO algorithm. Second, this controller's performance is improved by adding the Complementary Controller employing an ANN. 5. Training data for the ANN is created by solving optimal control problems. The ANN is activated when the swing angle constraint is about to be violated. It is trained using optimal control values corresponding to the cases where the swing angle falls in a particular band about the upper swing angle constraint. Simulations are performed in a MATLAB environment. Finally, some of the simulation results are validated on a physical system.
Citation - WoS: 0
Citation - Scopus: 0
Design and Implementation of Visual Simultaneous Localization and Mapping (VSLAM) Navigation System
(Ieee, 2021) Bekcan, Arda; Ergezer, Halit; 293396; Mekatronik Mühendisliği
It is very important to guess the location of the redetected objects and loop closures with the visual simultaneous localization and mapping system (VSLAM), one of the biggest problems of a mobile robot. VSLAM makes it possible to eliminate and/or reduce these applications' errors and realize or improve the robot's direction and position correctly by creating a map of the environment. This study aims to achieve an autonomous indoor/outdoor navigation of a ground robot using VSLAM algorithm in an unknown environment using a monocular camera. In this context, the theoretical information was tested in real-world conditions. Performance of localization and loop closing were compared based on the results obtained by experiments
Citation - WoS: 2
Citation - Scopus: 2
Development of air-to-ground engagement analysis model of fighter aircrafts
(Gazi Univ, Fac Engineering Architecture, 2022) Erdogan, Sinem; Bektas, Almila; Ergezer, Halit; 293396; Mekatronik Mühendisliği
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.
Durum ve İstikamet Referans Sisteminin Tasarımı ve Test Edilmesi
(2024) Altaş, İbrahim Ahmet; Ergezer, Halit; Mekatronik Mühendisliği
İnsansız Hava Araçları (İHA), günlük yaşantımızda yaygın hale gelmiş ve sayısız uygulamada kullanılan temel araçlar haline gelmiştir. İHA'lar gözetim, haritalama, kargo taşıma, güvenlik, tarım ve birçok başka alanda kullanılmaktadır. Konum tahmini İHA 'ların sistem döngüsündeki kritik bir aşamadır ve aracın üç boyutlu uzaydaki oryantasyonunu belirleme yeteneği sağlar. İHA konum tahmininin, sensör doğasından kaynaklanan zorlukları, sensör kısıtlamaları, çevresel faktörler ve hesaplamalı sınırlamalar da dahil olmak üzere ele alınmaktadır. Bu çalışma, İHA 'lar üzerinde hassas konum tahmini için kullanılan çeşitli teknikleri ve yöntemleri sunmaktadır. Ayrıca, sensör füzyonunun prensiplerine odaklanmış ve ivmelerölçer, jiroskop ve manyetometre gibi çeşitli sensörlerden gelen verilerin entegrasyonunu ve elde edilen sonuçların doğrulanması üzerine çalışılmıştır. Bu çalışma aynı zamanda konum tahmininin doğruluğunu artırmada kalibrasyon prosedürlerinin rolünü ele alır. Bu çalışmada, bir döner kanat insansız hava aracı modeli üzerine eklenen sensör modelleri üzerinden elde edilen veriler kullanılarak durum tahmini için bir füzyon algoritması tasarlanmıştır. Simülasyon ortamında tasarlanan sistemi uygulamak için gerçek sensörlerin kullanıldığı bir baskı devre kartı tasarlanmıştır. Bu kartı test etmek için kullanılacak bir İHA tasarlanmıştır ve İHA üzerinde kontrollü test çalışmaları yapabilemek için üç eksenli doğrulama platformunun çalışmaları tamamlamıştır.
Düşük Maliyetli Sensörler Kullanan Uyarlanabilir Tamamlayıcı Filtreye Dayalı Ahrs Tasarımı ve Uygulaması
(2024) Büyükbezirci, Sena; Ergezer, Halit; Mekatronik Mühendisliği
Durum ve İstikamet Referans Sistemleri (AHRS), havacılık sektöründe, insansız hava araçları ve robot uygulamalarında genellikle kullanılmaktadır. Bu tür bir ölçüm sisteminin temel ihtiyaçları hafif, kompakt, enerji açısından verimli ve kurulumunun kolay olmasıdır. Mikro-elektromekanik sistem (MEMS) teknolojisini kullanan uygun fiyatlı mikro atalet sensörlerinin artması nedeniyle, artık küçük ve ucuz tutum ölçüm sistemleri geliştirmek mümkün hale geldi. 'Tutum' terimi, bir aracın uzaydaki konumunu ifade eder. Durum ve İstikamet Referans Sistemleri (AHRS), bir aracın yönelimini belirlemek için kullanılan araçlardır. AHRS, jiroskop, ivmeölçer ve manyetometre gibi sıkça kullanılan sensörler kullanılarak oluşturulur. Bir AHRS genellikle üç eksende ivmeölçerler, jiroskoplar ve manyetometreler dahil olmak üzere yuvarlanma, eğim, sapma ve yönü ölçen sensörlerden oluşur. Allan Variance uygulamaları ve tamamlayıcı filtreleme gibi sensör birleştirme algoritmaları, tutum bilgilerini doğru bir şekilde tahmin etmek için AHRS'li sistemlerde kullanılabilir. Jiroskop, ivmeölçer ve manyetometre sensör verileri tamamlayıcı filtre ile birleştirildiğinde yuvarlanma, eğim, sapma ve yön bilgileri elde edilir. Sensör birleştirme algoritması, bir sensörün avantajını elde etmek ve diğer sensörün sınırlamalarını telafi etmek için her sensörün farklı güçlerinden yararlanacaktır.Tamamlayıcı filtrenin arkasındaki temel fikir, güvenilir sensöre daha fazla önem vererek önceliklendirmektir. Allan Varyansı, farklı stokastik süreçleri ve katsayılarını tanımlamak ve karakterize etmek için basit ve etkili bir yöntemdir. Allan sapmasının karakteristik eğrisi, sensör çıktısı üzerinde basit işlemlerle elde edilecek ve bu, verilerdeki hata türlerini ve büyüklüklerini belirlemek için kullanılacaktır. Allan varyansı, zaman alanındaki bir veri dizisini analiz etme yöntemidir ve ayrıca bir sistemdeki içsel gürültüyü ortalama zamanın bir fonksiyonu olarak belirlemek için de kullanılabilir. AHRS'nin birincil amacı, birden fazla sensörden gelen bilgileri entegre ederek ve işleyerek bir nesnenin yönelimi ve yönü hakkında doğru, gerçek zamanlı veriler sunmaktır. Bu yetenek, havacılık, denizcilik ve robotik gibi çeşitli alanlarda navigasyon, stabilizasyon ve kontrol için hayati öneme sahiptir.
Citation - WoS: 1
Citation - Scopus: 0
Evaluation of features used in electromyography classification
(Ieee, 2021) Alguner, Ayber Eray; Ergezer, Halit; 293396; Mekatronik Mühendisliği
Classification of electromyography (EMG) signals using machine learning has been studied for a long time. Today, this classification is tried to be made more accurate, fast and applicable by using the methods developed. However, beside this effort, it is suspected that researchers are using features without taking into account the effects on the classification performance, but often by influence of other researches. From this point of view, the effects of some features used in studies published in recent years on classification performance were tested and the results obtained were shared. In the experiments performed using a common method support vector machine (SVM), it was found that increasing the number of features does not always provide an increase in performance, even in some cases, it causes a decrease in accuracy rates.
Citation - WoS: 0
Citation - Scopus: 0
Formation Control of Fixed-Wing Uavs Using Mpc: Effect of Vehicle Speed
(Ieee, 2024) Ozcelik, Ozge Kartal; Ergezer, Halit; Mekatronik Mühendisliği
As UAV technology has progressed, its applications have expanded across various sectors such as Surveillance, Military, Maintenance, and Delivery. Due to their cost-effectiveness and safety in operations, UAVs have gained rapid popularity in these domains. Scenarios demanding the coordinated flight of multiple UAVs have become prevalent, leading to various formation types and control strategies tailored to specific use cases. Among these control mechanisms, Model Predictive Control (MPC) has found application in UAV formation flight. This study focuses on the development and implementation of MPC for the formation flight of fixed-wing UAVs. Experiments were conducted utilizing a setup involving three UAVs, elucidating the functionality of MPC and examining the impact of variations in the speed parameter on MPC performance.
Lpi Radar Waveform Classification Using Binary Svm And Multi-Class Svm Based On Principal Components Of Tfi
(2020) Ergezer, Halit; Bektas, Almila; 293396; Mekatronik Mühendisliği
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.
Citation - WoS: 3
Citation - Scopus: 5
Multi-Objective Trajectory Planning for Slung-Load Quadrotor System
(Ieee-inst Electrical Electronics Engineers inc, 2021) Ergezer, Halit; 293396; Mekatronik Mühendisliği
In this article, multi-objective trajectory planning has been carried out for a quadrotor carrying a slung load. The goal is to obtain non-dominated solutions for path length, mission duration, and dissipated energy cost functions. These costs are optimized by imposing constraints on the slung-load quadrotor system's endpoints, borders, obstacles, and dynamical equations. The dynamic model of a slung-load quadrotor system is used in the Euler-Lagrange formulation. Although the differential flatness feature is mostly used in this system's trajectory planning, a fully dynamic model has been used in our study. A new multi-objective Genetic Algorithm has been developed to solve path planning, aiming to optimize trajectory length, mission time, and energy consumed during the mission. The solution process has a three-phase algorithm: Phase-1 is about randomly generating waypoints, Phase-2 is about constructing the initial non-dominated pool, and the final phase, Phase-3, is obtaining the solution. In addition to conventional genetic operators, simple genetic operators are proposed to improve the trajectories locally. Pareto Fronts have been obtained corresponding to exciting scenarios. The method has been tested, and results have been presented at the end. A comparison of the solutions obtained with MOGA operators and MOPSO over hypervolume values is also presented.
Citation - WoS: 4
Citation - Scopus: 7
Online path planning for unmanned aerial vehicles to maximize instantaneous information
(Sage Publications inc, 2021) Ergezer, Halit; Leblebicioglu, Kemal; 293396; Mekatronik Mühendisliği
In this article, an online path planning algorithm for multiple unmanned aerial vehicles (UAVs) has been proposed. The aim is to gather information from target areas (desired regions) while avoiding forbidden regions in a fixed time window starting from the present time. Vehicles should not violate forbidden zones during a mission. Additionally, the significance and reliability of the information collected about a target are assumed to decrease with time. The proposed solution finds each vehicle's path by solving an optimization problem over a planning horizon while obeying specific rules. The basic structure in our solution is the centralized task assignment problem, and it produces near-optimal solutions. The solution can handle moving, pop-up targets, and UAV loss. It is a complicated optimization problem, and its solution is to be produced in a very short time. To simplify the optimization problem and obtain the solution in nearly real time, we have developed some rules. Among these rules, there is one that involves the kinematic constraints in the construction of paths. There is another which tackles the real-time decision-making problem using heuristics imitating human- like intelligence. Simulations are realized in MATLAB environment. The planning algorithm has been tested on various scenarios, and the results are presented.
Citation - WoS: 3
Citation - Scopus: 5
Two Majority Voting Classifiers Applied to Heart Disease Prediction
(Mdpi, 2023) Karadeniz, Talha; Maras, Hadi Hakan; Tokdemir, Gul; Ergezer, Halit; 34410; 293396; Bilgisayar Mühendisliği; Mekatronik Mühendisliği; Yazılım Mühendisliği
Two novel methods for heart disease prediction, which use the kurtosis of the features and the Maxwell-Boltzmann distribution, are presented. A Majority Voting approach is applied, and two base classifiers are derived through statistical weight calculation. First, exploitation of attribute kurtosis and attribute Kolmogorov-Smirnov test (KS test) result is done by plugging the base categorizer into a Bagging Classifier. Second, fitting Maxwell random variables to the components and summating KS statistics are used for weight assignment. We have compared state-of-the-art methods to the proposed classifiers and reported the results. According to the findings, our Gaussian distribution and kurtosis-based Majority Voting Bagging Classifier (GKMVB) and Maxwell Distribution-based Majority Voting Bagging Classifier (MKMVB) outperform SVM, ANN, and Naive Bayes algorithms. In this context, which also indicates, especially when we consider that the KS test and kurtosis hack is intuitive, that the proposed routine is promising. Following the state-of-the-art, the experiments were conducted on two well-known datasets of Heart Disease Prediction, namely Statlog, and Spectf. A comparison of Optimized Precision is made to prove the effectiveness of the methods: the newly proposed methods attained 85.6 and 81.0 for Statlog and Spectf, respectively (while the state of the heart attained 83.5 and 71.6, respectively). We claim that the Majority Voting family of classifiers is still open to new developments through appropriate weight assignment. This claim is obvious, especially when its simple structure is fused with the Ensemble Methods' generalization ability and success.
Citation - WoS: 2
Citation - Scopus: 3
Window length insensitive real-time EMG hand gesture classification using entropy calculated from globally parsed histograms
(Sage Publications Ltd, 2023) Alguner, Ayber Eray; Ergezer, Halit; 293396; Mekatronik Mühendisliği
Electromyography (EMG) signal classification is vital to diagnose musculoskeletal abnormalities and control devices by motion intention detection. Machine learning assists both areas by classifying conditions or motion intentions. This paper proposes a novel window length insensitive EMG classification method utilizing the Entropy feature. The main goal of this study is to show that entropy can be used as the only feature for fast real-time classification of EMG signals of hand gestures. The main goal of this study is to show that entropy can be used as the only feature for fast real-time classification of EMG signals of hand gestures. Additionally, the entropy feature can classify feature vectors of different sliding window lengths without including them in the training data. Many kinds of entropy feature succeeded in electroencephalography (EEG) and electrocardiography (ECG) classification research. However, to the best of our knowledge, the Entropy Feature proposed by Shannon stays untested for EMG classification to this day. All the machine learning models are tested on datasets NinaPro DB5 and the newly collected SingleMyo. As an initial analysis to test the entropy feature, classic Machine Learning (ML) models are trained on the NinaPro DB5 dataset. This stage showed that except for the K Nearest Neighbor (kNN) with high inference time, Support Vector Machines (SVM) gave the best validation accuracy. Later, SVM models trained with feature vectors created by 1 s (200 samples) sliding windows are tested on feature vectors created by 250 ms (50 samples) to 1500 ms (300 samples) sliding windows. This experiment resulted in slight accuracy differences through changing window length, indicating that the Entropy feature is insensitive to this parameter. Lastly, Locally Parsed Histogram (LPH), typical in standard entropy functions, makes learning hard for ML methods. Globally Parsed Histogram (GPH) was proposed, and classification accuracy increased from 60.35% to 89.06% while window length insensitivity is preserved. This study shows that Shannon's entropy is a compelling feature with low window length sensitivity for EMG hand gesture classification. The effect of the GPH approach against an easy-to-make mistake LPH is shown. A real-time classification algorithm for the entropy features is tested on the newly created SingleMyo dataset.