{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,10]],"date-time":"2026-06-10T03:54:53Z","timestamp":1781063693856,"version":"3.54.1"},"reference-count":39,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2021,3,10]],"date-time":"2021-03-10T00:00:00Z","timestamp":1615334400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Unmanned Aerial Vehicles (UAVs) are widely available in the current market to be used either for recreation as a hobby or to serve specific industrial requirements, such as agriculture and construction. However, illegitimate and criminal usage of UAVs is also on the rise which introduces their effective identification and detection as a research challenge. This paper proposes a novel machine learning-based for efficient identification and detection of UAVs. Specifically, an improved UAV identification and detection approach is presented using an ensemble learning based on the hierarchical concept, along with pre-processing and feature extraction stages for the Radio Frequency (RF) data. Filtering is applied on the RF signals in the detection approach to improve the output. This approach consists of four classifiers and they are working in a hierarchical way. The sample will pass the first classifier to check the availability of the UAV, and then it will specify the type of the detected UAV using the second classifier. The last two classifiers will handle the sample that is related to Bebop and AR to specify their mode. Evaluation of the proposed approach with publicly available dataset demonstrates better efficiency compared to existing detection systems in the literature. It has the ability to investigate whether a UAV is flying within the area or not, and it can directly identify the type of UAV and then the flight mode of the detected UAV with accuracy around 99%.<\/jats:p>","DOI":"10.3390\/s21061947","type":"journal-article","created":{"date-parts":[[2021,3,10]],"date-time":"2021-03-10T20:51:42Z","timestamp":1615409502000},"page":"1947","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":114,"title":["RF-Based UAV Detection and Identification Using Hierarchical Learning Approach"],"prefix":"10.3390","volume":"21","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3712-3405","authenticated-orcid":false,"given":"Ibrahim","family":"Nemer","sequence":"first","affiliation":[{"name":"Department of Computer Engineering, King Fahd University of Petroleum and Minerals, Dhahran 31261, Saudi Arabia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7879-1469","authenticated-orcid":false,"given":"Tarek","family":"Sheltami","sequence":"additional","affiliation":[{"name":"Department of Computer Engineering, King Fahd University of Petroleum and Minerals, Dhahran 31261, Saudi Arabia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8311-1731","authenticated-orcid":false,"given":"Irfan","family":"Ahmad","sequence":"additional","affiliation":[{"name":"Department of Information and Computer Science, King Fahd University of Petroleum and Minerals, Dhahran 31261, Saudi Arabia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ansar Ul-Haque","family":"Yasar","sequence":"additional","affiliation":[{"name":"Transportation Research Institute, Hasselt University, BE3500 Hasselt, Belgium"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9326-8409","authenticated-orcid":false,"given":"Mohammad A. R.","family":"Abdeen","sequence":"additional","affiliation":[{"name":"The Faculty of Computer and Information Systems, Islamic University of Madinah, Madinah 42351, Saudi Arabia"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2021,3,10]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Crommelinck, S., Bennett, R., Gerke, M., Nex, F., Yang, M.Y., and Vosselman, G. (2016). Review of automatic feature extraction from high-resolution optical sensor data for UAV-based cadastral mapping. Remote Sens., 8.","DOI":"10.3390\/rs8080689"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Puliti, S., Talbot, B., and Astrup, R. (2018). Tree-stump detection, segmentation, classification, and measurement using unmanned aerial vehicle (UAV) imagery. Forests, 9.","DOI":"10.3390\/f9030102"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Masuda, K., and Uchiyama, K. (2018). 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