{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,6]],"date-time":"2026-06-06T07:36:22Z","timestamp":1780731382653,"version":"3.54.1"},"reference-count":26,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2023,4,22]],"date-time":"2023-04-22T00:00:00Z","timestamp":1682121600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Ministry of Science and ICT (MSIT), South Korea","award":["IITP-2023-2018-0-01396"],"award-info":[{"award-number":["IITP-2023-2018-0-01396"]}]},{"name":"Ministry of Science and ICT (MSIT), South Korea","award":["S3098815"],"award-info":[{"award-number":["S3098815"]}]},{"name":"Information Technology Research Center (ITRC)","award":["IITP-2023-2018-0-01396"],"award-info":[{"award-number":["IITP-2023-2018-0-01396"]}]},{"name":"Information Technology Research Center (ITRC)","award":["S3098815"],"award-info":[{"award-number":["S3098815"]}]},{"name":"Ministry of SMEs and Startups (MSS, Korea)","award":["IITP-2023-2018-0-01396"],"award-info":[{"award-number":["IITP-2023-2018-0-01396"]}]},{"name":"Ministry of SMEs and Startups (MSS, Korea)","award":["S3098815"],"award-info":[{"award-number":["S3098815"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>The security and privacy risks posed by unmanned aerial vehicles (UAVs) have become a significant cause of concern in today\u2019s society. Due to technological advancement, these devices are becoming progressively inexpensive, which makes them convenient for many different applications. The massive number of UAVs is making it difficult to manage and monitor them in restricted areas. In addition, other signals using the same frequency range make it more challenging to identify UAV signals. In these circumstances, an intelligent system to detect and identify UAVs is a necessity. Most of the previous studies on UAV identification relied on various feature-extraction techniques, which are computationally expensive. Therefore, this article proposes an end-to-end deep-learning-based model to detect and identify UAVs based on their radio frequency (RF) signature. Unlike existing studies, multiscale feature-extraction techniques without manual intervention are utilized to extract enriched features that assist the model in achieving good generalization capability of the signal and making decisions with lower computational time. Additionally, residual blocks are utilized to learn complex representations, as well as to overcome vanishing gradient problems during training. The detection and identification tasks are performed in the presence of Bluetooth and WIFI signals, which are two signals from the same frequency band. For the identification task, the model is evaluated for specific devices, as well as for the signature of the particular manufacturers. The performance of the model is evaluated across various different signal-to-noise ratios (SNR). Furthermore, the findings are compared to the results of previous work. The proposed model yields an overall accuracy, precision, sensitivity, and F1-score of 97.53%, 98.06%, 98.00%, and 98.00%, respectively, for RF signal detection from 0 dB to 30 dB SNR in the CardRF dataset. The proposed model demonstrates an inference time of 0.37 ms (milliseconds) for RF signal detection, which is a substantial improvement over existing work. Therefore, the proposed end-to-end deep-learning-based method outperforms the existing work in terms of performance and time complexity. Based on the outcomes illustrated in the paper, the proposed model can be used in surveillance systems for real-time UAV detection and identification.<\/jats:p>","DOI":"10.3390\/s23094202","type":"journal-article","created":{"date-parts":[[2023,4,24]],"date-time":"2023-04-24T03:04:08Z","timestamp":1682305448000},"page":"4202","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":48,"title":["RF-Enabled Deep-Learning-Assisted Drone Detection and Identification: An End-to-End Approach"],"prefix":"10.3390","volume":"23","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3742-6692","authenticated-orcid":false,"given":"Syed Samiul","family":"Alam","sequence":"first","affiliation":[{"name":"Department of Electronic Engineering, Kookmin University, Seoul 02707, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5922-4410","authenticated-orcid":false,"given":"Arbil","family":"Chakma","sequence":"additional","affiliation":[{"name":"Department of Electronic Engineering, Kookmin University, Seoul 02707, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6422-5645","authenticated-orcid":false,"given":"Md Habibur","family":"Rahman","sequence":"additional","affiliation":[{"name":"Department of Electronic Engineering, Kookmin University, Seoul 02707, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9827-8374","authenticated-orcid":false,"given":"Raihan","family":"Bin Mofidul","sequence":"additional","affiliation":[{"name":"Department of Electronic Engineering, Kookmin University, Seoul 02707, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2877-5345","authenticated-orcid":false,"given":"Md Morshed","family":"Alam","sequence":"additional","affiliation":[{"name":"Department of Electronic Engineering, Kookmin University, Seoul 02707, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9926-9020","authenticated-orcid":false,"given":"Ida Bagus Krishna Yoga","family":"Utama","sequence":"additional","affiliation":[{"name":"Department of Electronic Engineering, Kookmin University, Seoul 02707, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9963-303X","authenticated-orcid":false,"given":"Yeong Min","family":"Jang","sequence":"additional","affiliation":[{"name":"Department of Electronic Engineering, Kookmin University, Seoul 02707, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,4,22]]},"reference":[{"key":"ref_1","unstructured":"Vemula, H. (2022, December 09). Multiple Drone Detection and Acoustic Scene Classification with Deep Learning. Brows. All Theses Diss. Available online: https:\/\/corescholar.libraries.wright.edu\/etd_all\/2221."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Wilson, R.L. (2014, January 23\u201324). Ethical issues with use of Drone aircraft. Proceedings of the International Symposium on Ethics in Science, Technology and Engineering, Chicago, IL, USA.","DOI":"10.1109\/ETHICS.2014.6893424"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"3159","DOI":"10.1080\/01431161.2017.1292074","article-title":"Lightweight UAV digital elevation models and orthoimagery for environmental applications: Data accuracy evaluation and potential for river flood risk modelling","volume":"38","author":"Coveney","year":"2017","journal-title":"Int. J. Remote Sens."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Alsalam, B.H.Y., Morton, K., Campbell, D., and Gonzalez, F. (2017, January 4\u201311). Autonomous UAV with vision based on-board decision making for remote sensing and precision agriculture. Proceedings of the Aerospace Conference, Big Sky, MT, USA.","DOI":"10.1109\/AERO.2017.7943593"},{"key":"ref_5","unstructured":"(2022, December 09). Amazon Prime Air Drone Delivery Fleet Gets FAA Approval. Available online: https:\/\/www.cnbc.com\/2020\/08\/31\/amazon-prime-now-drone-delivery-fleet-gets-faa-approval.html."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"101569","DOI":"10.1016\/j.pmcj.2022.101569","article-title":"Wavelet transform analytics for RF-based UAV detection and identification system using machine learning","volume":"82","author":"Medaiyese","year":"2022","journal-title":"Pervasive Mob. Comput."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"249","DOI":"10.1109\/MNET.011.2000648","article-title":"On the Localization of Wireless Targets: A Drone Surveillance Perspective","volume":"35","author":"Bisio","year":"2021","journal-title":"IEEE Netw."},{"key":"ref_8","unstructured":"(2022, December 11). Civilian Drone Crashes into Army Helicopter. Available online: https:\/\/nypost.com\/2017\/09\/22\/army-helicopter-hit-by-drone."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Birch, G.C., Griffin, J.C., and Erdman, M.K. (2015). UAS Detection Classification and Neutralization: Market Survey 2015, Sandia National Lab.","DOI":"10.2172\/1222445"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"49696","DOI":"10.1109\/ACCESS.2022.3172787","article-title":"RF-UAVNet: High-Performance Convolutional Network for RF-Based Drone Surveillance Systems","volume":"10","author":"Pham","year":"2022","journal-title":"IEEE Access"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"669","DOI":"10.1007\/s12065-020-00544-z","article-title":"Clustering method and sine cosine algorithm for image segmentation","volume":"15","author":"Khrissi","year":"2022","journal-title":"Evol. Intell."},{"key":"ref_12","first-page":"423","article-title":"An Efficient Image Clustering Technique based on Fuzzy C-means and Cuckoo Search Algorithm","volume":"12","author":"Khrissi","year":"2021","journal-title":"Int. J. Adv. Comput. Sci. Appl."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Ali, S.N., Shuvo, S.B., Al-Manzo, M.I.S., Hasan, M., and Hasan, T. (2023). An End-to-end Deep Learning Framework for Real-Time Denoising of Heart Sounds for Cardiac Disease Detection in Unseen Noise. TechRxiv.","DOI":"10.36227\/techrxiv.19950155"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Casabianca, P., and Zhang, Y. (2021). Acoustic-Based UAV Detection Using Late Fusion of Deep Neural Networks. Drones, 5.","DOI":"10.3390\/drones5030054"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"161431","DOI":"10.1109\/ACCESS.2021.3115805","article-title":"Deep Learning-Based Drone Classification Using Radar Cross Section Signatures at mmWave Frequencies","volume":"9","author":"Fu","year":"2021","journal-title":"IEEE Access"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"115928","DOI":"10.1016\/j.eswa.2021.115928","article-title":"Single and multiple drones detection and identification using RF based deep learning algorithm","volume":"187","author":"Pokrajac","year":"2022","journal-title":"Expert Syst. Appl."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"104313","DOI":"10.1016\/j.dib.2019.104313","article-title":"DroneRF dataset: A dataset of drones for RF-based detection, classification and identification","volume":"26","author":"Allahham","year":"2019","journal-title":"Data Br."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Mo, Y., Huang, J., and Qian, G. (2022). Deep Learning Approach to UAV Detection and Classification by Using Compressively Sensed RF Signal. Sensors, 22.","DOI":"10.3390\/s22083072"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"176","DOI":"10.1109\/JRFID.2022.3157653","article-title":"Hierarchical Learning Framework for UAV Detection and Identification","volume":"6","author":"Medaiyese","year":"2022","journal-title":"IEEE J. Radio Freq. Identif."},{"key":"ref_20","unstructured":"Iandola, F.N., Han, S., Moskewicz, M.W., Ashraf, K., Dally, W.J., and Keutzer, K. (2016). SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5 MB model size. arXiv."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"151754","DOI":"10.1109\/ACCESS.2019.2947510","article-title":"Plant leaves classification: A few-shot learning method based on siamese network","volume":"7","author":"Wang","year":"2019","journal-title":"IEEE Access"},{"key":"ref_22","unstructured":"(2022, December 12). Cardinal RF (CardRF): An Outdoor UAV\/UAS\/Drone RF Signals with Bluetooth and WiFi Signals Dataset|IEEE DataPort. Available online: https:\/\/ieee-dataport.org\/documents\/cardinal-rf-cardrf-outdoor-uavuasdrone-rf-signals-bluetooth-and-wifi-signals-dataset."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"173377","DOI":"10.1109\/ACCESS.2019.2956725","article-title":"Multiscale Residual Convolution Neural Network and Sector Descriptor-Based Road Detection Method","volume":"7","author":"Dai","year":"2019","journal-title":"IEEE Access"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Coletti, M., Lunga, D., Bassett, J.K., and Rose, A. (2019, January 17). Evolving larger convolutional layer kernel sizes for a settlement detection deep-learner on summit. Proceedings of the Third Workshop on Deep Learning on Supercomputers (DLS), Denver, CO, USA.","DOI":"10.1109\/DLS49591.2019.00010"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"51909","DOI":"10.1007\/s11356-022-18849-0","article-title":"A new pairwise deep learning feature for environmental microorganism image analysis","volume":"29","author":"Kulwa","year":"2022","journal-title":"Environ. Sci. Pollut. Res."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"204","DOI":"10.1016\/j.bbe.2021.12.010","article-title":"SVIA dataset: A new dataset of microscopic videos and images for computer-aided sperm analysis","volume":"42","author":"Chen","year":"2022","journal-title":"Biocybern. Biomed. Eng."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/9\/4202\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T19:21:25Z","timestamp":1760124085000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/9\/4202"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,4,22]]},"references-count":26,"journal-issue":{"issue":"9","published-online":{"date-parts":[[2023,5]]}},"alternative-id":["s23094202"],"URL":"https:\/\/doi.org\/10.3390\/s23094202","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,4,22]]}}}