{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,2]],"date-time":"2026-01-02T07:08:56Z","timestamp":1767337736914,"version":"build-2065373602"},"reference-count":47,"publisher":"MDPI AG","issue":"20","license":[{"start":{"date-parts":[[2020,10,19]],"date-time":"2020-10-19T00:00:00Z","timestamp":1603065600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"GIPSA-lab","award":["CLAAS_T project"],"award-info":[{"award-number":["CLAAS_T project"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>In the last years, the commercial drone\/unmanned aerial vehicles market has grown due to their technological performances (provided by the multiple onboard available sensors), low price, and ease of use. Being very attractive for an increasing number of applications, their presence represents a major issue for public or classified areas with a special status, because of the rising number of incidents. Our paper proposes a new approach for the drone movement detection and characterization based on the ultra-wide band (UWB) sensing system and advanced signal processing methods. This approach characterizes the movement of the drone using classical methods such as correlation, envelope detection, time-scale analysis, but also a new method, the recurrence plot analysis. The obtained results are compared in terms of movement map accuracy and required computation time in order to offer a future starting point for the drone intrusion detection.<\/jats:p>","DOI":"10.3390\/s20205904","type":"journal-article","created":{"date-parts":[[2020,10,19]],"date-time":"2020-10-19T20:44:41Z","timestamp":1603140281000},"page":"5904","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":16,"title":["New Approach of UAV Movement Detection and Characterization Using Advanced Signal Processing Methods Based on UWB Sensing"],"prefix":"10.3390","volume":"20","author":[{"given":"Angela","family":"Digulescu","sequence":"first","affiliation":[{"name":"Telecommunications and Information Technology Department, Military Technical Academy \u201cFerdinand I\u201d, 050141 Bucharest, Romania"},{"name":"GIPSA-Lab, Universit\u00e9 Grenoble Alpes, 38400 Saint Martin d\u2019H\u00e8res, France"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1132-8741","authenticated-orcid":false,"given":"Cristina","family":"Despina-Stoian","sequence":"additional","affiliation":[{"name":"Telecommunications and Information Technology Department, Military Technical Academy \u201cFerdinand I\u201d, 050141 Bucharest, Romania"},{"name":"Lab-STICC, CNRS, UMR 6285, Universit\u00e9 de Bretagne Occidentale, 29200 Brest, France"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Denis","family":"St\u0103nescu","sequence":"additional","affiliation":[{"name":"Telecommunications and Information Technology Department, Military Technical Academy \u201cFerdinand I\u201d, 050141 Bucharest, Romania"},{"name":"GIPSA-Lab, Universit\u00e9 Grenoble Alpes, 38400 Saint Martin d\u2019H\u00e8res, France"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Florin","family":"Popescu","sequence":"additional","affiliation":[{"name":"Telecommunications and Information Technology Department, Military Technical Academy \u201cFerdinand I\u201d, 050141 Bucharest, Romania"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Florin","family":"Enache","sequence":"additional","affiliation":[{"name":"Telecommunications and Information Technology Department, Military Technical Academy \u201cFerdinand I\u201d, 050141 Bucharest, Romania"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Cornel","family":"Ioana","sequence":"additional","affiliation":[{"name":"GIPSA-Lab, Universit\u00e9 Grenoble Alpes, 38400 Saint Martin d\u2019H\u00e8res, France"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0819-6285","authenticated-orcid":false,"given":"Emanuel","family":"R\u0103doi","sequence":"additional","affiliation":[{"name":"Lab-STICC, CNRS, UMR 6285, Universit\u00e9 de Bretagne Occidentale, 29200 Brest, France"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Iulian","family":"R\u00eencu","sequence":"additional","affiliation":[{"name":"Telecommunications and Information Technology Department, Military Technical Academy \u201cFerdinand I\u201d, 050141 Bucharest, Romania"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Alexandru","family":"\u0218erb\u0103nescu","sequence":"additional","affiliation":[{"name":"Telecommunications and Information Technology Department, Military Technical Academy \u201cFerdinand I\u201d, 050141 Bucharest, Romania"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2020,10,19]]},"reference":[{"key":"ref_1","unstructured":"(2020, September 21). UAS Sightings Report, Available online: https:\/\/www.faa.gov\/uas\/resources\/public_records\/uas_sightings_report\/."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Carvalho, R., Nascimento, R., D\u2019Angelo, T., Delabrida, S., GC Bianchi, A., Oliveira, R.A., Azp\u00farua, H., and Uzeda Garcia, L.G. (2020). A UAV-Based Framework for Semi-Automated Thermographic Inspection of Belt Conveyors in the Mining Industry. Sensors, 20.","DOI":"10.3390\/s20082243"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Garcia-Aunon, P., del Cerro, J., and Barrientos, A. (2019). Behavior-Based Control for an Aerial Robotic Swarm in Surveillance Missions. Sensors, 19.","DOI":"10.3390\/s19204584"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"T\u00f6r\u00f6k, \u00c1., B\u00f6g\u00f6ly, G., Somogyi, \u00c1., and Lovas, T. (2020). Application of UAV in Topographic Modelling and Structural Geological Mapping of Quarries and Their Surroundings\u2014Delineation of Fault-Bordered Raw Material Reserves. Sensors, 20.","DOI":"10.3390\/s20020489"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"48572","DOI":"10.1109\/ACCESS.2019.2909530","article-title":"Unmanned Aerial Vehicles (UAVs): A Survey on Civil Applications and Key Research Challenges","volume":"7","author":"Shakhatreh","year":"2019","journal-title":"IEEE Access"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"157","DOI":"10.3849\/aimt.01233","article-title":"State of the Art and Problems of Defeat of Low, Slow and Small Unmanned Aerial Vehicles","volume":"13","author":"Dudush","year":"2018","journal-title":"Adv. Mil. Technol."},{"key":"ref_7","unstructured":"(2020, September 21). Worldwide Drone Incidents. Available online: https:\/\/www.dedrone.com\/resources\/incidents\/all."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"138669","DOI":"10.1109\/ACCESS.2019.2942944","article-title":"Machine Learning-Based Drone Detection and Classification: State-of-the-Art in Research","volume":"7","author":"Taha","year":"2019","journal-title":"IEEE Access"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Seidaliyeva, U., Akhmetov, D., Ilipbayeva, L., and Matson, E.T. (2020). Real-Time and Accurate Drone Detection in a Video with a Static Background. Sensors, 20.","DOI":"10.3390\/s20143856"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Oh, H.M., Lee, H., and Kim, M.Y. (2019, January 15\u201318). Comparing Convolutional Neural Network (CNN) models for machine learning-based drone and bird classification of anti-drone system. Proceedings of the 19th International Conference on Control, Automation and Systems (ICCAS), Jeju, Korea.","DOI":"10.23919\/ICCAS47443.2019.8971699"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"7","DOI":"10.1186\/s41074-019-0059-x","article-title":"Deep learning-based strategies for the detection and tracking of drones using several cameras","volume":"11","author":"Unlu","year":"2019","journal-title":"IPSJ Trans. Comput. Vis. Appl."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"61639","DOI":"10.1109\/ACCESS.2019.2915944","article-title":"Deep Learning-Based Super-Resolution Reconstruction and Marker Detection for Drone Landing","volume":"7","author":"Truong","year":"2019","journal-title":"IEEE Access"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"130697","DOI":"10.1109\/ACCESS.2020.3009518","article-title":"TIB-Net: Drone Detection Network With Tiny Iterative Backbone","volume":"8","author":"Sun","year":"2020","journal-title":"IEEE Access"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"2526","DOI":"10.1109\/TVT.2019.2893615","article-title":"Machine Learning Inspired Sound-Based Amateur Drone Detection for Public Safety Applications","volume":"68","author":"Anwar","year":"2019","journal-title":"IEEE Trans. Veh. Technol."},{"key":"ref_15","unstructured":"Jeon, S., Shin, J., Lee, Y., Kim, W., Kwon, Y., and Yang, H. (September, January 28). Empirical study of drone sound detection in real-life environment with deep neural networks. Proceedings of the 25th European Signal Processing Conference (EUSIPCO), Kos Island, Greece."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"509","DOI":"10.1109\/JCN.2018.000075","article-title":"Hidden Markov model based drone sound recognition using MFCC technique in practical noisy environments","volume":"20","author":"Shi","year":"2018","journal-title":"J. Commun. Netw."},{"key":"ref_17","first-page":"5078","article-title":"SVM-based Drone Sound Recognition using the Combination of HLA and WPT Techniques in Practical Noisy Environment","volume":"13","author":"He","year":"2019","journal-title":"KSII Trans. Internet Inf. Syst."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Jamil, S., Rahman, M., Ullah, A., Badnava, S., Forsat, M., and Mirjavadi, S.S. (2020). Malicious UAV Detection Using Integrated Audio and Visual Features for Public Safety Applications. Sensors, 20.","DOI":"10.3390\/s20143923"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Liu, H., Wei, Z., Chen, Y., Pan, J., Lin, L., and Ren, Y. (2017, January 19\u201321). Drone Detection Based on an Audio-Assisted Camera Array. Proceedings of the 2017 IEEE Third International Conference on Multimedia Big Data (BigMM), Laguna Hills, CA, USA.","DOI":"10.1109\/BigMM.2017.57"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"60","DOI":"10.1109\/OJCOMS.2019.2955889","article-title":"Detection and Classification of UAVs Using RF Fingerprints in the Presence of Wi-Fi and Bluetooth Interference","volume":"1","author":"Ezuma","year":"2020","journal-title":"IEEE Open J. Commun. Soc."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"112","DOI":"10.1109\/MCOM.2018.1700424","article-title":"Low-Complexity Portable Passive Drone Surveillance via SDR-Based Signal Processing","volume":"56","author":"Fu","year":"2018","journal-title":"IEEE Commun. Mag."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"401","DOI":"10.1109\/OJCOMS.2020.2984312","article-title":"Non-Cooperative Low-Complexity Detection Approach for FHSS-GFSK Drone Control Signals","volume":"1","author":"Mototolea","year":"2020","journal-title":"IEEE Open J. Commun. Soc."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"43","DOI":"10.1109\/MCOM.2017.1700450","article-title":"Technologies for Efficient Amateur Drone Detection in 5G Millimeter-Wave Cellular Infrastructure","volume":"56","author":"Solomitckii","year":"2018","journal-title":"IEEE Commun. Mag."},{"key":"ref_24","first-page":"3862","article-title":"An Improved RF Detection Algorithm Using EMD-based WT","volume":"13","author":"Lv","year":"2019","journal-title":"KSII Trans. Internet Inf. Syst."},{"key":"ref_25","unstructured":"Guo, X., Ng, C.S., de Jong, E., and Smits, A.B. (2019, January 2\u20134). Micro-Doppler Based Mini-UAV Detection with Low-Cost Distributed Radar in Dense Urban Environment. Proceedings of the 2019 16th European Radar Conference (EuRAD), Paris, France."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Choi, B., Oh, D., Kim, S., Chong, J.-W., and Li, Y.-C. (2018). Long-Range Drone Detection of 24 G FMCW Radar with E-plane Sectoral Horn Array. Sensors, 18.","DOI":"10.3390\/s18124171"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"1858","DOI":"10.1109\/TMTT.2019.2961911","article-title":"Range-Doppler Map Improvement in FMCW Radar for Small Moving Drone Detection Using the Stationary Point Concentration Technique","volume":"68","author":"Park","year":"2020","journal-title":"IEEE Trans. Microw. Theory Tech."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Coluccia, A., Parisi, G., and Fascista, A. (2020). Detection and Classification of Multirotor Drones in Radar Sensor Networks: A Review. Sensors, 20.","DOI":"10.3390\/s20154172"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"57526","DOI":"10.1109\/ACCESS.2018.2873571","article-title":"Ultra-Wideband Based Pose Estimation for Small Unmanned Aerial Vehicles","volume":"6","author":"Strohmeier","year":"2018","journal-title":"IEEE Access"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"23","DOI":"10.1142\/S2301385016400033","article-title":"Ultra-Wideband-Based Localization for Quadcopter Navigation","volume":"4","author":"Kexin","year":"2016","journal-title":"Unmanned Syst."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Liu, F., Zhang, J., Wang, J., Han, H., and Yang, D. (2020). An UWB\/Vision Fusion Scheme for Determining Pedestrians\u2019 Indoor Location. Sensors, 20.","DOI":"10.3390\/s20041139"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Ma, L., Liu, M., Wang, N., Wang, L., Yang, Y., and Wang, H. (2020). Room-Level Fall Detection Based on Ultra-Wideband (UWB) Monostatic Radar and Convolutional Long Short-Term Memory (LSTM). Sensors, 20.","DOI":"10.3390\/s20041105"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Oppermann, I., Hamalainen, M., and Iinatti, J. (2004). UWB Theory and Applications, John Wiley & Sons Ltd.","DOI":"10.1002\/0470869194"},{"key":"ref_34","unstructured":"(2020, September 14). FCC ID. Available online: https:\/\/fccid.io\/NUF-P440-A\/User-Manual\/User-Manual-2878444.pdf."},{"key":"ref_35","unstructured":"(2020, September 14). FCC, Available online: https:\/\/apps.fcc.gov\/els\/GetAtt.html?id=187726&x."},{"key":"ref_36","unstructured":"(2020, September 14). Support Parrot. Available online: https:\/\/support.parrot.com\/us\/support\/products\/mambo-fpv."},{"key":"ref_37","unstructured":"Marendi\u0107, A., and Zrinjski, M.-Z. (2016, January 20\u201322). Unmanned Aerial Photogrammetric Systems in the Service of Engineering Geodesy. Proceedings of the International Symposium on Engineering Geodesy\u2014SIG 2016, Vara\u017edin, Croatia."},{"key":"ref_38","unstructured":"Semmlow, J. (2005). Circuits, Signals, and Systems for Bioengineers, Academic Press. [1st ed.]."},{"key":"ref_39","unstructured":"Sklar, B. (2006). Digital Communications. Fundamentals and Applications, Prentice Hall PTR. [2nd ed.]."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"399","DOI":"10.1016\/S0010-4825(01)00009-9","article-title":"The use of the Hilbert transform in ECG signal analysis","volume":"31","author":"Benitez","year":"2001","journal-title":"Comput. Biol. Med."},{"key":"ref_41","unstructured":"Mallat, S. (2008). A Wavelet Tour of Signal Processing, Academic Press. [3rd ed.]."},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Digulescu, A., Paun, M., Vasile, C., Petrut, T., Deacu, D., Ioana, C., and Tamas, R. (2014, January 13\u201316). Electrical arc surveillance and localization system based on advanced signal processing techniques. Proceedings of the 2014 IEEE International Energy Conference (ENERGYCON), Cavtat, Croatia.","DOI":"10.1109\/ENERGYCON.2014.6850462"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"19","DOI":"10.1007\/978-3-319-29922-8_2","article-title":"Applications of Transient Signal Analysis Using the Concept of Recurrence Plot Analysis","volume":"180","author":"Digulescu","year":"2016","journal-title":"Recurr. Plots Their Quantif. Expand. Horiz."},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Digulescu, A., Ioana, C., and Serbanescu, A. (2019). Phase Diagram-Based Sensing with Adaptive Waveform Design and Recurrent States Quantification for the Instantaneous Frequency Law Tracking. Sensors, 19.","DOI":"10.3390\/s19112434"},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Cai, L., Shi, W., Miao, Z., and Hao, M. (2018). Accuracy Assessment Measures for Object Extraction from Remote Sensing Images. Remote Sens., 10.","DOI":"10.3390\/rs10020303"},{"key":"ref_46","unstructured":"Skolnik, M. (2008). Radar Handbook, McGraw-Hill. [3rd ed.]."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"66","DOI":"10.1016\/j.comcom.2020.03.017","article-title":"Unmanned aerial vehicle for internet of everything: Opportunities and challenges","volume":"155","author":"Liu","year":"2020","journal-title":"Comput. Commun."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/20\/20\/5904\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T10:24:12Z","timestamp":1760178252000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/20\/20\/5904"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,10,19]]},"references-count":47,"journal-issue":{"issue":"20","published-online":{"date-parts":[[2020,10]]}},"alternative-id":["s20205904"],"URL":"https:\/\/doi.org\/10.3390\/s20205904","relation":{},"ISSN":["1424-8220"],"issn-type":[{"type":"electronic","value":"1424-8220"}],"subject":[],"published":{"date-parts":[[2020,10,19]]}}}