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    Detection, identification, and direction of arrival estimation of drone FHSS signals with uniform linear antenna array
    (IEEE, 2021) Kaplan, Batuhan; Kahraman, İbrahim; Ekti, Ali Rıza; Yarkan, Serhan; Görçin, Ali; Özdemir, Mehmet Kemal; Çırpan, Hakan Ali
    Safety, security, and privacy are three critical concerns affiliated with the use of drones in everyday life. Considering their ever-shrinking sizes and capabilities, being aware of drone activities in the vicinity becomes an important surveillance item. Therefore, keeping track of drones and preferably their controllers should be included into the already-existing security measures. In this study, a frequency hopping spread spectrum (FHSS) type drone controller signal detection and emitter direction finding framework is proposed to achieve aforementioned goals. Since drone communications signals generally coexist with other FHSS signals in 2.4 GHz industrial, scientific, and medical (ISM) band, first, a method based on cyclostationarity analysis is proposed to distinguish the drone radio controller signals from other signals utilizing 2.4 GHz ISM band. Then, a variant of short-term Fourier transform is introduced to extract the parameters of detected drone remote controller signals. The correct hopping signals are then aggregated based on the clustered parameters to obtain their combined baseband equivalent signal. Furthermore, the resampling process is applied to reduce the unrelated samples in the spectrum and represent the spectrum with the reconstructed signal, which has a much lower bandwidth than the spread bandwidth. Finally, two different multiple signal classification algorithms are utilized to estimate the direction of the drone controller relative to the receiving system. In order to validate the overall performance of the proposed method, the introduced framework is implemented on hardware platforms and tested under real-world conditions. A uniform linear antenna array is utilized to capture over-the-air signals in hilly terrain suburban environments by considering both line–of-sight and non–line–of-sight cases. Direction estimation performance is presented in a comparative manner and relevant discussions are provided.
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    Measurement based direction of arrival estimation for frequency hopping signals
    (IEEE, 2020) Kaplan, Batuhan; Kahraman, İbrahim; Ekti, Ali Riza; Yarkan, Serhan; Çırpan, Hakan Ali
    In this paper, the problem of measurement-based angle of arrival estimation for frequency hopping signals is discussed. While the angle of arrival estimation is important information for many subjects, obtaining the angle of arrival estimation for frequency hopping signals is needed a different approach. A method based on resampling and noise reduction is proposed upon the reconstruction of the wideband received frequency hopping signal under the real-world conditions by using time-frequency analysis. Then, multiple signal classification (MUSIC) algorithm is used to obtain measurement based angle of arrival estimation of frequency hopping signals. The effectiveness of the proposed method is shown by the results of testing for two different location points.
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    Measurement-Based Large Scale Statistical Modeling of Air–to–Air Wireless UAV Channels via Novel Time–Frequency Analysis
    (IEEE, 2021) Ede, Burak; Kaplan, Batuhan; Kahraman, İbrahim; Keşir, Samed; Yarkan, Serhan; Ekti, Ali Rıza; Baykaş, Tunçer; Görçin, Ali; Çırpan, Hakan Ali
    Any operation scenario for unmanned aerial vehicles also known as drones in real world requires resilient wireless link to guarantee capacity and performance for users, which can only be achieved by obtaining detailed knowledge about the propagation channel. Thus, this study investigates the largescale channel propagation statistics for the line of sight air–to–air (A2A) drone communications to estimate the path loss exponent (PLE). We conducted a measurement campaign at 5.8 GHz, using low cost and light weight software defined radio based channel sounder which is developed in this study and then further integrated on commercially available drones. To determine the PLE, frequency-based, time-based and time–frequency based methods are utilized. Accuracy of the proposed method is verified under ideal conditions in a well-isolated anechoic chamber before the actual measurement campaign to verify the performance in a free space path loss environment. The path loss exponent for A2A wireless drone channel is estimated with these verified methods.