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Article Citation - WoS: 1Citation - Scopus: 1On the Accuracy of an Emitter Localization Method Based on Multipath Exploitation in Realistic Scenarios(Taylor & Francis Ltd, 2022) Al Imran, M. A.; Ank, E.; Dalveren, Y.; Tabakcioglu, M. B.; Kara, A.This study aims to evaluate the accuracy of a method proposed for passive localization of radar emitters around irregular terrains with a single receiver in Electronic Support Measures systems. Previously, the authors targeted only the theoretical development of the localization method. In fact, this could be a serious concern in practice since there is no evidence regarding its accuracy under the real data gathered from realistic scenarios. Therefore, an accurate ray-tracing algorithm is adapted to enable the implementation of the method in practice. Then, realistic scenarios are determined based on the geographic information system map generated to collect high-resolution digital terrain elevation data, as well as realistic localization problems for radar emitters. Next, simulations are performed to test the localization method. Thus, the performance of the method is verified for practical implementation in the electronic warfare context for the first time. Lastly, the performance bounds of the method are discussed.Article Citation - WoS: 6Citation - Scopus: 7A Simplified Method Based on Rssi Fingerprinting for Iot Device Localization in Smart Cities(Ieee-inst Electrical Electronics Engineers inc, 2024) Dogan, Deren; Dalveren, Yaser; Kara, Ali; Derawi, MohammadThe Internet of Things (IoT) has significantly improved location-based services in smart cities, such as automated public transportation and traffic management. Estimating the location of connected devices is a critical problem. Low Power Wide Area Network (LPWAN) technologies are used for localization due to their low power consumption and long communication range. Recent advances in Machine Learning have made Received Signal Strength Indicator (RSSI) fingerprinting with LPWAN technologies effective. However, this requires a connection between devices and gateways or base stations, which can increase network deployment, maintenance, and installation costs. This study proposes a cost-effective RSSI fingerprinting solution using IQRF technology for IoT device localization. The region of interest is divided into grids to provide training locations, and measurements are conducted to create a training dataset containing RSSI fingerprints. Pattern matching is performed to localize the device by comparing the fingerprint of the end device with the fingerprints in the created database. To evaluate the efficiency of the proposed solution, measurements were conducted in a short-range local area ( $80\times 30$ m) at 868 MHz. In the measurements, four IQRF nodes were utilized to receive the RSSIs from a transmitting IQRF node. The performances of well-known ML classifiers on the created dataset are then comparatively assessed in terms of test accuracy, prediction speed, and training time. According to the results, the Bagged Trees classifier demonstrated the highest accuracy with 96.87%. However, with an accuracy of 95.69%, the Weighted k-NN could also be a reasonable option for real-world implementations due to its faster prediction speed (37615 obs/s) and lower training time (28.1 s). To the best of the authors' knowledge, this is the first attempt to explore the feasibility of the IQRF networks to develop a RSSI fingerprinting-based IoT device localization in the literature. The promising results suggest that the proposed method could be used as a low-cost alternative for IoT device localization in short-range location-based smart city applications.Conference Object Citation - WoS: 3Comparative Analysis of Tdoa-Based Localization Methods in the Presence of Sensor Position Errors(Ieee, 2017) Dalveren, Yaser; Kara, AliIt is widely known that localization of emitters can be efficiently achieved by time difference of arrival (TDOA) techniques in a multiple sensor system. Several studies have been proposed in the literature to improve the localization accuracy of TDOA techniques. Among these, very few of them have considered the error in the sensor positions although the accuracy of localization is very sensitive to sensor position errors. In this study, existing TDOA-based localization methods in the presence of sensor position errors are briefly discussed, and then they are comparatively analyzed for specific scenarios. To this end, simulations are performed to compare the localization accuracy of the methods, specifically, with high level of sensor positional errors. It is intended to decide an efficient and robust estimator to be used for an ongoing research on passive localization of radar emitters in dense scattering environments.Conference Object Citation - Scopus: 6A Mini-Review on Radio Frequency Fingerprinting Localization in Outdoor Environments: Recent Advances and Challenges(Institute of Electrical and Electronics Engineers Inc., 2022) Dogan,D.; Dalveren,Y.; Kara,A.A considerable growth in demand for locating the source of emissions in outdoor environments has led to the rapid development of various localization methods. Among these, RF fingerprinting (RFF) localization has become one of the most promising method due to its unique advantages resulted from the recent developments in machine learning techniques. In this short review, it is aimed to assess the existing RFF methods in the literature for outdoor localization. For this purpose, firstly, the current state of RFF localization methods in outdoor environments are overviewed. Then, the main research challenges in the development of RFF localization are highlighted. This is followed by a brief discussion on the open issues in order to give future research directions. Furthermore, the research efforts currently undertaken by the authors are briefly addressed. © 2022 IEEE.Article Radar Emitter Localization Based on Multipath Exploitation Using Machine Learning(Ieee-inst Electrical Electronics Engineers inc, 2024) Catak, Ferhat Ozgur; Al Imran, Md Abdullah; Dalveren, Yaser; Yildiz, Beytullah; Kara, AliIn this study, a Machine Learning (ML)-based approach is proposed to enhance the computational efficiency of a particular method that was previously proposed by the authors for passive localization of radar emitters based on multipath exploitation with a single receiver in Electronic Support Measures (ESM) systems. The idea is to utilize a ML model on a dataset consisting of useful features obtained from the priori-known operational environment. To verify the applicability and computational efficiency of the proposed approach, simulations are performed on the pseudo-realistic scenes to create the datasets. Well-known regression ML models are trained and tested on the created datasets. The performance of the proposed approach is then evaluated in terms of localization accuracy and computational speed. Based on the results, it is verified that the proposed approach is computationally efficient and implementable in radar detection applications on the condition that the operational environment is known prior to implementation.
