Detection, identification, and direction of arrival estimation of drone FHSS signals with uniform linear antenna array

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dc.authorid0000-0002-3591-6567en_US
dc.authorid0000-0003-0368-0374en_US
dc.authorid0000-0002-9054-0005en_US
dc.authorid0000-0001-6430-3009en_US
dc.authorid0000-0001-8670-9034en_US
dc.contributor.authorKaplan, Batuhan
dc.contributor.authorKahraman, İbrahim
dc.contributor.authorEkti, Ali Rıza
dc.contributor.authorYarkan, Serhan
dc.contributor.authorGörçin, Ali
dc.contributor.authorÖzdemir, Mehmet Kemal
dc.contributor.authorÇırpan, Hakan Ali
dc.date.accessioned2021-12-02T10:33:57Z
dc.date.available2021-12-02T10:33:57Z
dc.date.issued2021en_US
dc.departmentFakülteler, Mühendislik Fakültesi, Elektrik-Elektronik Mühendisliği Bölümüen_US
dc.description.abstractSafety, 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.en_US
dc.identifier.doi10.1109/ACCESS.2021.3127199en_US
dc.identifier.scopus2-s2.0-85119397558en_US
dc.identifier.scopusqualityN/Aen_US
dc.identifier.urihttps://hdl.handle.net/11467/5096
dc.identifier.urihttps://doi.org/10.1109/ACCESS.2021.3127199
dc.identifier.volume9en_US
dc.identifier.wosWOS:000719557200001en_US
dc.identifier.wosqualityQ2en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherIEEEen_US
dc.relation.ispartofIEEE Accessen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectSTFTen_US
dc.subjectCyclostationarityen_US
dc.subjectDirection Findingen_US
dc.subjectFHSSen_US
dc.subjectMUSICen_US
dc.subjectDrone Remote Controlleren_US
dc.titleDetection, identification, and direction of arrival estimation of drone FHSS signals with uniform linear antenna arrayen_US
dc.typeArticleen_US

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