{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,26]],"date-time":"2026-03-26T07:09:31Z","timestamp":1774508971894,"version":"3.50.1"},"reference-count":51,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2019,12,19]],"date-time":"2019-12-19T00:00:00Z","timestamp":1576713600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJGI"],"abstract":"<jats:p>Spoofing of Unmanned Aerial Vehicles (UAV) is generally carried out through spoofing of the UAV\u2019s Global Positioning System (GPS) receiver. This paper presents a vision-based UAV spoofing detection method that utilizes Visual Odometry (VO). This method is independent of the other complementary sensors and any knowledge or archived map and datasets. The proposed method is based on the comparison of relative sub-trajectory of the UAV from VO, with its absolute replica from GPS within a moving window along the flight path. The comparison is done using three dissimilarity measures including (1) Sum of Euclidian Distances between Corresponding Points (SEDCP), (2) angle distance and (3) taxicab distance between the Histogram of Oriented Displacements (HOD) of these sub-trajectories. This method can determine the time and location of UAV spoofing and bounds the drift error of VO. It can be used without any restriction in the usage environment and can be implemented in real-time applications. This method is evaluated on four UAV spoofing scenarios. The results indicate that this method is effective in the detection of UAV spoofing due to the Sophisticated Receiver-Based (SRB) GPS spoofing. This method can detect UAV spoofing in the long-range UAV flights when the changes in UAV flight direction is larger than 3\u00b0 and in the incremental UAV spoofing with the redirection rate of 1\u00b0. Additionally, using SEDCP, the spoofing of the UAV, when there is no redirection and only the velocity of the UAV is changed, can be detected. The results show that SEDCP is more effective in the detection of UAV spoofing and fake GPS positions.<\/jats:p>","DOI":"10.3390\/ijgi9010006","type":"journal-article","created":{"date-parts":[[2019,12,20]],"date-time":"2019-12-20T09:50:33Z","timestamp":1576835433000},"page":"6","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":47,"title":["Spoofing Detection of Civilian UAVs Using Visual Odometry"],"prefix":"10.3390","volume":"9","author":[{"given":"Masood","family":"Varshosaz","sequence":"first","affiliation":[{"name":"Geomatics Engineering Faculty, K.N. Toosi University of Technology, Valiasr St., Tehran 1543319967, Iran"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Alireza","family":"Afary","sequence":"additional","affiliation":[{"name":"Geomatics Engineering Faculty, K.N. Toosi University of Technology, Valiasr St., Tehran 1543319967, Iran"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5894-5969","authenticated-orcid":false,"given":"Barat","family":"Mojaradi","sequence":"additional","affiliation":[{"name":"Department of Geomatics, School of Civil Engineering, Iran University of Science and Technology, Hengam St., Tehran 1311416846, Iran"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mohammad","family":"Saadatseresht","sequence":"additional","affiliation":[{"name":"School of Surveying and Geospatial Engineering, College of Engineering, The University of Tehran, North Kargar St., Tehran 1439957131, Iran"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ebadat","family":"Ghanbari Parmehr","sequence":"additional","affiliation":[{"name":"Department of Geomatics, Faculty of Civil Engineering, Babol Noshirvani University of Technology, Shariati St., Babol 4714871167, Iran"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2019,12,19]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Leick, A., Rapoport, L., and Tatarnikov, D. 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