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Article Citation - WoS: 6Citation - Scopus: 6Opportunities and Challenges Inrcsmeasurement of 9-Mm Bullet Model With77 Ghzmmwavecotsradar Systems(Wiley, 2020) Ahmed, Badar-ud-din; Kara, Ali; Zencir, Ertan; Benzaghta, MohamedThis article indicates a thus far unexplored area of applied research and development to the application and system engineers and researchers from broad engineering backgrounds. Results of a study are presented for measurement of calibrated Radar Cross Section (RCS) of a 9-mm bullet (projectile) model by using a commercial-of-the-shelf (COTS) millimeter wave Frequency Modulated Continuous Wave (FMCW) radar system operating in 77 to 81 GHz frequency range. The calibrated RCS variation against the aspect angle is measured experimentally, analyzed, and compared with the simulation results which shows fair matching between the two. The opportunities and challenges attached with the use of such COTS systems for development of Hostile Fire Indication (HFI) systems are discussed. This bullet type and this mmwave frequency has not been thus far studied and reported in literature. This may motivate interested individuals/entities to try to measure (at acceptable accuracy before anechoic chamber measurements) RCS of similar low-size objects by using such low-cost COTS platforms.Article Citation - WoS: 7Citation - Scopus: 7Towards Mmwave Altimetry for Uas: Exploring the Potential of 77 Ghz Automotive Radars(Mdpi, 2024) Awan, Maaz Ali; Dalveren, Yaser; Kara, Ali; Derawi, MohammadPrecise altitude data are indispensable for flight navigation, particularly during the autonomous landing of unmanned aerial systems (UASs). Conventional light and barometric sensors employed for altitude estimation are limited by poor visibility and temperature conditions, respectively, whilst global positioning system (GPS) receivers provide the altitude from the mean sea level (MSL) marred with a slow update rate. To cater to the landing safety requirements, UASs necessitate precise altitude information above ground level (AGL) impervious to environmental conditions. Radar altimeters, a mainstay in commercial aviation for at least half a century, realize these requirements through minimum operational performance standards (MOPSs). More recently, the proliferation of 5G technology and interference with the universally allocated band for radar altimeters from 4.2 to 4.4 GHz underscores the necessity to explore novel avenues. Notably, there is no dedicated MOPS tailored for radar altimeters of UASs. To gauge the performance of a radar altimeter offering for UASs, existing MOPSs are the de facto choice. Historically, frequency-modulated continuous wave (FMCW) radars have been extensively used in a broad spectrum of ranging applications including radar altimeters. Modern monolithic millimeter wave (mmWave) automotive radars, albeit designed for automotive applications, also employ FMCW for precise ranging with a cost-effective and compact footprint. Given the technology maturation with excellent size, weight, and power (SWaP) metrics, there is a growing trend in industry and academia to explore their efficacy beyond the realm of the automotive industry. To this end, their feasibility for UAS altimetry remains largely untapped. While the literature on theoretical discourse is prevalent, a specific focus on mmWave radar altimetry is lacking. Moreover, clutter estimation with hardware specifications of a pure look-down mmWave radar is unreported. This article argues the applicability of MOPSs for commercial aviation for adaptation to a UAS use case. The theme of the work is a tutorial based on a simplified mathematical and theoretical discussion on the understanding of performance metrics and inherent intricacies. A systems engineering approach for deriving waveform specifications from operational requirements of a UAS is offered. Lastly, proposed future research directions and insights are included.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.
