{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,5]],"date-time":"2026-06-05T07:34:39Z","timestamp":1780644879360,"version":"3.54.1"},"reference-count":178,"publisher":"MDPI AG","issue":"22","license":[{"start":{"date-parts":[[2019,11,6]],"date-time":"2019-11-06T00:00:00Z","timestamp":1572998400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100000780","name":"European Commission","doi-asserted-by":"publisher","award":["740859"],"award-info":[{"award-number":["740859"]}],"id":[{"id":"10.13039\/501100000780","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100000780","name":"European Commission","doi-asserted-by":"publisher","award":["676157"],"award-info":[{"award-number":["676157"]}],"id":[{"id":"10.13039\/501100000780","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Usage of Unmanned Aerial Vehicles (UAVs) is growing rapidly in a wide range of consumer applications, as they prove to be both autonomous and flexible in a variety of environments and tasks. However, this versatility and ease of use also brings a rapid evolution of threats by malicious actors that can use UAVs for criminal activities, converting them to passive or active threats. The need to protect critical infrastructures and important events from such threats has brought advances in counter UAV (c-UAV) applications. Nowadays, c-UAV applications offer systems that comprise a multi-sensory arsenal often including electro-optical, thermal, acoustic, radar and radio frequency sensors, whose information can be fused to increase the confidence of threat\u2019s identification. Nevertheless, real-time surveillance is a cumbersome process, but it is absolutely essential to detect promptly the occurrence of adverse events or conditions. To that end, many challenging tasks arise such as object detection, classification, multi-object tracking and multi-sensor information fusion. In recent years, researchers have utilized deep learning based methodologies to tackle these tasks for generic objects and made noteworthy progress, yet applying deep learning for UAV detection and classification is considered a novel concept. Therefore, the need to present a complete overview of deep learning technologies applied to c-UAV related tasks on multi-sensor data has emerged. The aim of this paper is to describe deep learning advances on c-UAV related tasks when applied to data originating from many different sensors as well as multi-sensor information fusion. This survey may help in making recommendations and improvements of c-UAV applications for the future.<\/jats:p>","DOI":"10.3390\/s19224837","type":"journal-article","created":{"date-parts":[[2019,11,7]],"date-time":"2019-11-07T06:52:36Z","timestamp":1573109556000},"page":"4837","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":190,"title":["Deep Learning on Multi Sensor Data for Counter UAV Applications\u2014A Systematic Review"],"prefix":"10.3390","volume":"19","author":[{"given":"Stamatios","family":"Samaras","sequence":"first","affiliation":[{"name":"Centre for Research and Technology Hellas, Information Technologies Institute, 6th km Charilaou-Thermi, 57001 Thermi, Greece"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Eleni","family":"Diamantidou","sequence":"additional","affiliation":[{"name":"Centre for Research and Technology Hellas, Information Technologies Institute, 6th km Charilaou-Thermi, 57001 Thermi, Greece"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dimitrios","family":"Ataloglou","sequence":"additional","affiliation":[{"name":"Centre for Research and Technology Hellas, Information Technologies Institute, 6th km Charilaou-Thermi, 57001 Thermi, Greece"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nikos","family":"Sakellariou","sequence":"additional","affiliation":[{"name":"Centre for Research and Technology Hellas, Information Technologies Institute, 6th km Charilaou-Thermi, 57001 Thermi, Greece"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1102-5708","authenticated-orcid":false,"given":"Anastasios","family":"Vafeiadis","sequence":"additional","affiliation":[{"name":"Centre for Research and Technology Hellas, Information Technologies Institute, 6th km Charilaou-Thermi, 57001 Thermi, Greece"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Vasilis","family":"Magoulianitis","sequence":"additional","affiliation":[{"name":"Centre for Research and Technology Hellas, Information Technologies Institute, 6th km Charilaou-Thermi, 57001 Thermi, Greece"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Antonios","family":"Lalas","sequence":"additional","affiliation":[{"name":"Centre for Research and Technology Hellas, Information Technologies Institute, 6th km Charilaou-Thermi, 57001 Thermi, Greece"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2763-4217","authenticated-orcid":false,"given":"Anastasios","family":"Dimou","sequence":"additional","affiliation":[{"name":"Centre for Research and Technology Hellas, Information Technologies Institute, 6th km Charilaou-Thermi, 57001 Thermi, Greece"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9649-9306","authenticated-orcid":false,"given":"Dimitrios","family":"Zarpalas","sequence":"additional","affiliation":[{"name":"Centre for Research and Technology Hellas, Information Technologies Institute, 6th km Charilaou-Thermi, 57001 Thermi, Greece"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6381-8326","authenticated-orcid":false,"given":"Konstantinos","family":"Votis","sequence":"additional","affiliation":[{"name":"Centre for Research and Technology Hellas, Information Technologies Institute, 6th km Charilaou-Thermi, 57001 Thermi, Greece"},{"name":"Institute For the Future, University of Nicosia, Makedonitissis 46, 2417 Nicosia, Cyprus"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Petros","family":"Daras","sequence":"additional","affiliation":[{"name":"Centre for Research and Technology Hellas, Information Technologies Institute, 6th km Charilaou-Thermi, 57001 Thermi, Greece"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dimitrios","family":"Tzovaras","sequence":"additional","affiliation":[{"name":"Centre for Research and Technology Hellas, Information Technologies Institute, 6th km Charilaou-Thermi, 57001 Thermi, Greece"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2019,11,6]]},"reference":[{"key":"ref_1","unstructured":"Group, T. (2019, April 24). World Civil Unmanned Aerial Systems Market Profile and Forecast. Available online: http:\/\/tealgroup.com\/images\/TGCTOC\/WCUAS2017TOC_EO.pdf."},{"key":"ref_2","unstructured":"Research, G.V. (2019, April 24). Commercial UAV Market Analysis By Product (Fixed Wing, Rotary Blade, Nano, Hybrid), By Application (Agriculture, Energy, Government, Media and Entertainment) In addition, Segment Forecasts to 2022. Available online: https:\/\/www.grandviewresearch.com\/industry-analysis\/commercial-uav-market."},{"key":"ref_3","unstructured":"Guardian, T. (2019, May 06). Gatwick Drone Disruption Cost Airport Just \u00a31.4 m. Available online: https:\/\/www.theguardian.com\/uk-news\/2019\/jun\/18\/gatwick-drone-disruption-cost-airport-just-14m."},{"key":"ref_4","unstructured":"(2019, May 06). Anti-Drone. Anti-Drone System Overview and Technology Comparison. Available online: https:\/\/anti-drone.eu\/blog\/anti-drone-publications\/anti-drone-system-overview-and-technology-comparison.html."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Liggins II, M., Hall, D., and Llinas, J. (2017). Handbook of Multisensor Data Fusion: Theory and Practice, CRC Press.","DOI":"10.1201\/9781420053098"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"436","DOI":"10.1038\/nature14539","article-title":"Deep learning","volume":"521","author":"LeCun","year":"2015","journal-title":"Nature"},{"key":"ref_7","first-page":"40","article-title":"Deep convolutional neural networks: Structure, feature extraction and training","volume":"20","year":"2017","journal-title":"Inf. Technol. Manag. Sci."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Zhao, Z.Q., Zheng, P., Xu, S.T., and Wu, X. (2019). Object detection with deep learning: A review. arXiv.","DOI":"10.1109\/TNNLS.2018.2876865"},{"key":"ref_9","unstructured":"Fiaz, M., Mahmood, A., and Jung, S.K. (2018). Tracking Noisy Targets: A Review of Recent Object Tracking Approaches. arXiv."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"8","DOI":"10.1109\/MGRS.2017.2762307","article-title":"Deep learning in remote sensing: A comprehensive review and list of resources","volume":"5","author":"Zhu","year":"2017","journal-title":"IEEE Geosci. Remote Sens. Mag."},{"key":"ref_11","unstructured":"(2019, June 15). Google Scholar Search. Available online: https:\/\/scholar.google.gr\/schhp?hl=en."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"28","DOI":"10.1016\/j.inffus.2011.08.001","article-title":"Multisensor data fusion: A review of the state-of-the-art","volume":"14","author":"Khaleghi","year":"2013","journal-title":"Inf. Fusion"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Hu, D., Wang, C., Nie, F., and Li, X. (2019, January 12\u201317). Dense Multimodal Fusion for Hierarchically Joint Representation. Proceedings of the ICASSP 2019\u20132019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Brighton, UK.","DOI":"10.1109\/ICASSP.2019.8683898"},{"key":"ref_14","unstructured":"Liu, K., Li, Y., Xu, N., and Natarajan, P. (2018). Learn to Combine Modalities in Multimodal Deep Learning. arXiv."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Knott, E.F., Schaeffer, J.F., and Tulley, M.T. (2004). Radar Cross Section, SciTech Publishing.","DOI":"10.1049\/SBRA026E"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"435","DOI":"10.1017\/S1759078714000282","article-title":"Classification of small UAVs and birds by micro-Doppler signatures","volume":"6","author":"Molchanov","year":"2014","journal-title":"Int. J. Microw. Wirel. Technol."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Tait, P. (2005). Introduction to Radar Target Recognition, IET.","DOI":"10.1049\/PBRA018E"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Jokanovic, B., Amin, M., and Ahmad, F. (2016, January 1\u20136). Radar fall motion detection using deep learning. Proceedings of the 2016 IEEE Radar Conference (RadarConf), Philadelphia, PA, USA.","DOI":"10.1109\/RADAR.2016.7485147"},{"key":"ref_19","unstructured":"De Wit, J.M., Harmanny, R., and Premel-Cabic, G. (November, January 31). Micro-Doppler analysis of small UAVs. Proceedings of the 2012 9th European Radar Conference, Amsterdam, The Netherlands."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Harmanny, R., De Wit, J., and Cabic, G.P. (2014, January 11\u201313). Radar micro-Doppler feature extraction using the spectrogram and the cepstrogram. Proceedings of the 2014 11th European Radar Conference, Cincinnati, OH, USA.","DOI":"10.1109\/EuRAD.2014.6991233"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"De Wit, J., Harmanny, R., and Molchanov, P. (2014, January 13\u201317). Radar micro-Doppler feature extraction using the singular value decomposition. Proceedings of the 2014 International Radar Conference, Lille, France.","DOI":"10.1109\/RADAR.2014.7060268"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Fuhrmann, L., Biallawons, O., Klare, J., Panhuber, R., Klenke, R., and Ender, J. (2017, January 28\u201330). Micro-Doppler analysis and classification of UAVs at Ka band. Proceedings of the 2017 18th International Radar Symposium (IRS), Prague, Czech Republic.","DOI":"10.23919\/IRS.2017.8008142"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"225","DOI":"10.1016\/j.patcog.2017.04.024","article-title":"Regularized 2D complex-log spectral analysis and subspace reliability analysis of micro-Doppler signature for UAV detection","volume":"69","author":"Ren","year":"2017","journal-title":"Pattern Recognit."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"227","DOI":"10.1109\/LGRS.2017.2781711","article-title":"Micro-Doppler mini-UAV classification using empirical-mode decomposition features","volume":"15","author":"Oh","year":"2017","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Ma, X., Oh, B.S., Sun, L., Toh, K.A., and Lin, Z. (2018, January 20\u201324). EMD-Based Entropy Features for micro-Doppler Mini-UAV Classification. Proceedings of the 2018 24th International Conference on Pattern Recognition (ICPR), Beijing, China.","DOI":"10.1109\/ICPR.2018.8546180"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Sun, Y., Fu, H., Abeywickrama, S., Jayasinghe, L., Yuen, C., and Chen, J. (2018, January 19\u201321). Drone classification and localization using micro-doppler signature with low-frequency signal. Proceedings of the 2018 IEEE International Conference on Communication Systems (ICCS), Chengdu, China.","DOI":"10.1109\/ICCS.2018.8689237"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"1813","DOI":"10.1049\/el.2015.3038","article-title":"Classification of loaded\/unloaded micro-drones using multistatic radar","volume":"51","author":"Fioranelli","year":"2015","journal-title":"Electron. Lett."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Hoffmann, F., Ritchie, M., Fioranelli, F., Charlish, A., and Griffiths, H. (2016, January 1\u20136). Micro-Doppler based detection and tracking of UAVs with multistatic radar. Proceedings of the 2016 IEEE Radar Conference (RadarConf), Philadelphia, PA, USA.","DOI":"10.1109\/RADAR.2016.7485236"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Zhang, P., Yang, L., Chen, G., and Li, G. (2017, January 20). Classification of drones based on micro-Doppler signatures with dual-band radar sensors. Proceedings of the 2017 Progress in Electromagnetics Research Symposium-Fall (PIERS-FALL), Singapore.","DOI":"10.1109\/PIERS-FALL.2017.8293214"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"191","DOI":"10.1017\/aer.2018.158","article-title":"Classification of UAV and bird target in low-altitude airspace with surveillance radar data","volume":"123","author":"Chen","year":"2019","journal-title":"Aeronaut. J."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Messina, M., and Pinelli, G. (2019, January 23\u201325). Classification of Drones with a Surveillance Radar Signal. Proceedings of the 12th International Conference on Computer Vision Systems (ICVS), Thessaloniki, Greece.","DOI":"10.1007\/978-3-030-34995-0_66"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"1305","DOI":"10.1109\/LGRS.2016.2582538","article-title":"Classification of birds and UAVs based on radar polarimetry","volume":"13","author":"Torvik","year":"2016","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"38","DOI":"10.1109\/LGRS.2016.2624820","article-title":"Drone classification using convolutional neural networks with merged Doppler images","volume":"14","author":"Kim","year":"2016","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Mendis, G.J., Randeny, T., Wei, J., and Madanayake, A. (2016, January 1\u20133). Deep learning based doppler radar for micro UAS detection and classification. Proceedings of the MILCOM 2016-2016 IEEE Military Communications Conference, Baltimore, MD, USA.","DOI":"10.1109\/MILCOM.2016.7795448"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Wang, L., Tang, J., and Liao, Q. (2019). A Study on Radar Target Detection Based on Deep Neural Networks. IEEE Sens. Lett.","DOI":"10.1109\/LSENS.2019.2896072"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Stamatios Samaras, V.M., Anastasios Dimou, D.Z., and Daras, P. (2019, January 23\u201325). UAV classification with deep learning using surveillance radar data. Proceedings of the 12th International Conference on Computer Vision Systems (ICVS), Thessaloniki, Greece.","DOI":"10.1007\/978-3-030-34995-0_68"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Regev, N., Yoffe, I., and Wulich, D. (2017, January 23\u201326). Classification of single and multi propelled miniature drones using multilayer perceptron artificial neural network. Proceedings of the International Conference on Radar Systems (Radar 2017), Belfast, UK.","DOI":"10.1049\/cp.2017.0378"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Habermann, D., Dranka, E., Caceres, Y., and do Val, J.B. (2018, January 23\u201327). Drones and helicopters classification using point clouds features from radar. Proceedings of the 2018 IEEE Radar Conference (RadarConf18), Oklahoma City, OK, USA.","DOI":"10.1109\/RADAR.2018.8378565"},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Mohajerin, N., Histon, J., Dizaji, R., and Waslander, S.L. (2014, January 19\u201323). Feature extraction and radar track classification for detecting UAVs in civillian airspace. Proceedings of the 2014 IEEE Radar Conference, Cincinnati, OH, USA.","DOI":"10.1109\/RADAR.2014.6875676"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"2","DOI":"10.1109\/TAES.2006.1603402","article-title":"Micro-Doppler effect in radar: Phenomenon, model, and simulation study","volume":"42","author":"Chen","year":"2006","journal-title":"IEEE Trans. Aerosp. Electron. Syst."},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Al Hadhrami, E., Al Mufti, M., Taha, B., and Werghi, N. (2018, January 26\u201328). Transfer learning with convolutional neural networks for moving target classification with micro-Doppler radar spectrograms. Proceedings of the 2018 International Conference on Artificial Intelligence and Big Data (ICAIBD), Chengdu, China.","DOI":"10.1109\/ICAIBD.2018.8396184"},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Al Hadhrami, E., Al Mufti, M., Taha, B., and Werghi, N. (2018, January 15\u201317). Classification of ground moving radar targets using convolutional neural network. Proceedings of the 2018 22nd International Microwave and Radar Conference (MIKON), Pozna\u0144, Poland.","DOI":"10.23919\/MIKON.2018.8405154"},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Al Hadhrami, E., Al Mufti, M., Taha, B., and Werghi, N. (2018, January 20\u201322). Ground moving radar targets classification based on spectrogram images using convolutional neural networks. Proceedings of the 2018 19th International Radar Symposium (IRS), Bonn, Germany.","DOI":"10.23919\/IRS.2018.8447897"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"1002","DOI":"10.1109\/TGRS.2013.2246574","article-title":"Detection and extraction of target with micromotion in spiky sea clutter via short-time fractional Fourier transform","volume":"52","author":"Chen","year":"2013","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Tahmoush, D., and Silvious, J. (2009, January 28\u201330). Radar micro-Doppler for long range front-view gait recognition. Proceedings of the 2009 IEEE 3rd International Conference on Biometrics: Theory, Applications, and Systems, Washington, DC, USA.","DOI":"10.1109\/BTAS.2009.5339049"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"234","DOI":"10.1049\/iet-spr.2009.0072","article-title":"Analysis of radar human gait signatures","volume":"4","author":"Raj","year":"2010","journal-title":"IET Signal Process."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"1052","DOI":"10.1109\/JSEN.2018.2879223","article-title":"Potential Active Shooter Detection Based on Radar Micro-Doppler and Range\u2013Doppler Analysis Using Artificial Neural Network","volume":"19","author":"Li","year":"2018","journal-title":"IEEE Sens. J."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"1328","DOI":"10.1109\/TGRS.2009.2012849","article-title":"Human activity classification based on micro-Doppler signatures using a support vector machine","volume":"47","author":"Kim","year":"2009","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Ritchie, M., Fioranelli, F., Griffiths, H., and Torvik, B. (2015, January 10\u201315). Micro-drone RCS analysis. Proceedings of the 2015 IEEE Radar Conference, Arlington, VA, USA.","DOI":"10.1109\/RadarConf.2015.7411926"},{"key":"ref_50","first-page":"209","article-title":"The quefrency alanysis of time series for echoes; Cepstrum, pseudo-autocovariance, cross-cepstrum and saphe cracking","volume":"15","author":"Bogert","year":"1963","journal-title":"Time Ser. Anal."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"273","DOI":"10.1007\/BF00994018","article-title":"Support-vector networks","volume":"20","author":"Cortes","year":"1995","journal-title":"Mach. Learn."},{"key":"ref_52","unstructured":"Rish, I. (2001, January 4). An empirical study of the naive Bayes classifier. Proceedings of the IJCAI 2001 Workshop on Empirical Methods in Artificial Intelligence, Seattle, WA, USA."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"903","DOI":"10.1098\/rspa.1998.0193","article-title":"The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis","volume":"454","author":"Huang","year":"1998","journal-title":"Proc. R. Soc. Lond. Ser. A Math. Phys. Eng. Sci."},{"key":"ref_54","doi-asserted-by":"crossref","unstructured":"Hastie, T., Tibshirani, R., and Friedman, J. (2009). The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Springer.","DOI":"10.1007\/978-0-387-84858-7"},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"911","DOI":"10.1049\/iet-rsn.2018.0020","article-title":"Review of radar classification and RCS characterisation techniques for small UAVs ordrones","volume":"12","author":"Patel","year":"2018","journal-title":"IET Radar Sonar Navig."},{"key":"ref_56","unstructured":"Ghadaki, H., and Dizaji, R. (2006, January 24\u201327). Target track classification for airport surveillance radar (ASR). Proceedings of the 2006 IEEE Conference on Radar, Verona, NY, USA."},{"key":"ref_57","doi-asserted-by":"crossref","unstructured":"Lund\u00e9n, J., and Koivunen, V. (2016, January 2\u20136). Deep learning for HRRP-based target recognition in multistatic radar systems. Proceedings of the 2016 IEEE Radar Conference (RadarConf), Philadelphia, PA, USA.","DOI":"10.1109\/RADAR.2016.7485271"},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1186\/s13634-019-0603-y","article-title":"Convolutional neural networks for radar HRRP target recognition and rejection","volume":"2019","author":"Wan","year":"2019","journal-title":"EURASIP J. Adv. Signal Process."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"9191","DOI":"10.1109\/ACCESS.2019.2891594","article-title":"Radar HRRP Target Recognition Based on Deep One-Dimensional Residual-Inception Network","volume":"7","author":"Guo","year":"2019","journal-title":"IEEE Access"},{"key":"ref_60","doi-asserted-by":"crossref","unstructured":"El Housseini, A., Toumi, A., and Khenchaf, A. (2017, January 20\u201322). Deep Learning for target recognition from SAR images. Proceedings of the 2017 Seminar on Detection Systems Architectures and Technologies (DAT), Algiers, Algeria.","DOI":"10.1109\/DAT.2017.7889171"},{"key":"ref_61","doi-asserted-by":"crossref","unstructured":"Chen, S., and Wang, H. (November, January 30). SAR target recognition based on deep learning. Proceedings of the 2014 International Conference on Data Science and Advanced Analytics (DSAA), Shanghai, China.","DOI":"10.1109\/DSAA.2014.7058124"},{"key":"ref_62","doi-asserted-by":"crossref","unstructured":"Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., and Rabinovich, A. (2015, January 7\u201312). Going deeper with convolutions. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Boston, MA, USA.","DOI":"10.1109\/CVPR.2015.7298594"},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"1527","DOI":"10.1162\/neco.2006.18.7.1527","article-title":"A fast learning algorithm for deep belief nets","volume":"18","author":"Hinton","year":"2006","journal-title":"Neural Comput."},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"277","DOI":"10.1023\/A:1007662407062","article-title":"Large margin classification using the perceptron algorithm","volume":"37","author":"Freund","year":"1999","journal-title":"Mach. Learn."},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"1728","DOI":"10.1177\/0278364911405086","article-title":"Learning to close loops from range data","volume":"30","author":"Nieto","year":"2011","journal-title":"Int. J. Robot. Res."},{"key":"ref_66","unstructured":"Dizaji, R.M., and Ghadaki, H. (2009). Classification System for Radar and Sonar Applications. (7,567,203), U.S. Patent."},{"key":"ref_67","first-page":"1097","article-title":"Imagenet classification with deep convolutional neural networks","volume":"25","author":"Krizhevsky","year":"2012","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_68","doi-asserted-by":"crossref","unstructured":"Saqib, M., Khan, S.D., Sharma, N., and Blumenstein, M. (September, January 29). A study on detecting drones using deep convolutional neural networks. Proceedings of the 2017 14th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), Lecce, Italy.","DOI":"10.1109\/AVSS.2017.8078541"},{"key":"ref_69","unstructured":"Simonyan, K., and Zisserman, A. (2014). Very deep convolutional networks for large-scale image recognition. arXiv."},{"key":"ref_70","doi-asserted-by":"crossref","unstructured":"Zeiler, M.D., and Fergus, R. (2014, January 6\u201312). Visualizing and understanding convolutional networks. Proceedings of the European Conference on Computer Vision, Zurich, Switzerland.","DOI":"10.1007\/978-3-319-10590-1_53"},{"key":"ref_71","doi-asserted-by":"crossref","unstructured":"Mrunalini Nalamati, A.K., Muhammed Saqib, N.S., and Blumenstein, M. (2019, January 18\u201321). Drone Detection in Long-range Surveillance Videos. Proceedings of the 2019 16th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), Taiwan, China.","DOI":"10.1109\/AVSS.2019.8909830"},{"key":"ref_72","doi-asserted-by":"crossref","unstructured":"Schumann, A., Sommer, L., Klatte, J., Schuchert, T., and Beyerer, J. (September, January 29). Deep cross-domain flying object classification for robust UAV detection. Proceedings of the 2017 14th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), Lecce, Italy.","DOI":"10.1109\/AVSS.2017.8078558"},{"key":"ref_73","doi-asserted-by":"crossref","unstructured":"Craye, C., and Ardjoune, S. (2019, January 18\u201321). Spatio-temporal Semantic Segmentation for Drone Detection. Proceedings of the 2019 16th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), Taiwan, China.","DOI":"10.1109\/AVSS.2019.8909854"},{"key":"ref_74","doi-asserted-by":"crossref","unstructured":"Vasileios Magoulianitis, D.A., Anastasios Dimou, D.Z., and Daras, P. (2019, January 18\u201321). Does Deep Super-Resolution Enhance UAV Detection. Proceedings of the 2019 16th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), Taiwan, China.","DOI":"10.1109\/AVSS.2019.8909865"},{"key":"ref_75","doi-asserted-by":"crossref","unstructured":"Opromolla, R., Fasano, G., and Accardo, D. (2018). A Vision-Based Approach to UAV Detection and Tracking in Cooperative Applications. Sensors, 18.","DOI":"10.3390\/s18103391"},{"key":"ref_76","doi-asserted-by":"crossref","unstructured":"Aker, C., and Kalkan, S. (September, January 29). Using deep networks for drone detection. Proceedings of the 2017 14th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), Lecce, Italy.","DOI":"10.1109\/AVSS.2017.8078539"},{"key":"ref_77","unstructured":"Redmon, J., Divvala, S., Girshick, R., and Farhadi, A. (July, January 26). You only look once: Unified, real-time object detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA."},{"key":"ref_78","doi-asserted-by":"crossref","first-page":"879","DOI":"10.1109\/TPAMI.2016.2564408","article-title":"Detecting flying objects using a single moving camera","volume":"39","author":"Rozantsev","year":"2016","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_79","doi-asserted-by":"crossref","first-page":"23805","DOI":"10.3390\/s150923805","article-title":"Vision-based detection and distance estimation of micro unmanned aerial vehicles","volume":"15","author":"Kalkan","year":"2015","journal-title":"Sensors"},{"key":"ref_80","unstructured":"Chang, C.I. (2003). Hyperspectral Imaging: Techniques for Spectral Detection and Classification, Springer Science & Business Media."},{"key":"ref_81","doi-asserted-by":"crossref","first-page":"4377","DOI":"10.1038\/s41598-019-40066-y","article-title":"Early Detection of Tomato Spotted Wilt Virus by Hyperspectral Imaging and Outlier Removal Auxiliary Classifier Generative Adversarial Nets (OR-AC-GAN)","volume":"9","author":"Wang","year":"2019","journal-title":"Sci. Rep."},{"key":"ref_82","doi-asserted-by":"crossref","unstructured":"Lu, Y., Perez, D., Dao, M., Kwan, C., and Li, J. (2018, January 20\u201322). Deep learning with synthetic hyperspectral images for improved soil detection in multispectral imagery. Proceedings of the IEEE Ubiquitous Computing, Electronics & Mobile Communication Conference, New York, NY, USA.","DOI":"10.1109\/UEMCON.2018.8796838"},{"key":"ref_83","doi-asserted-by":"crossref","first-page":"476","DOI":"10.1016\/j.patcog.2017.11.024","article-title":"Material based salient object detection from hyperspectral images","volume":"76","author":"Liang","year":"2018","journal-title":"Pattern Recognit."},{"key":"ref_84","doi-asserted-by":"crossref","unstructured":"Al-Sarayreh, M., Reis, M.M., Yan, W.Q., and Klette, R. (2019, January 3\u20135). A Sequential CNN Approach for Foreign Object Detection in Hyperspectral Images. Proceedings of the International Conference on Computer Analysis of Images and Patterns, Salerno, Italy.","DOI":"10.1007\/978-3-030-29888-3_22"},{"key":"ref_85","doi-asserted-by":"crossref","first-page":"551","DOI":"10.1007\/s10846-017-0689-0","article-title":"Hyperspectral imaging for real-time unmanned aerial vehicle maritime target detection","volume":"90","author":"Freitas","year":"2018","journal-title":"J. Intell. Robot. Syst."},{"key":"ref_86","doi-asserted-by":"crossref","unstructured":"Pham, T., Takalkar, M., Xu, M., Hoang, D., Truong, H., Dutkiewicz, E., and Perry, S. (2019, January 1\u20134). Airborne Object Detection Using Hyperspectral Imaging: Deep Learning Review. Proceedings of the International Conference on Computational Science and Its Applications, Saint Petersburg, Russia.","DOI":"10.1007\/978-3-030-24289-3_23"},{"key":"ref_87","doi-asserted-by":"crossref","first-page":"24","DOI":"10.1109\/MSP.2013.2278915","article-title":"Detection algorithms in hyperspectral imaging systems: An overview of practical algorithms","volume":"31","author":"Manolakis","year":"2013","journal-title":"IEEE Signal Process. Mag."},{"key":"ref_88","doi-asserted-by":"crossref","first-page":"797","DOI":"10.1007\/s11263-018-1080-8","article-title":"Cluster sparsity field: An internal hyperspectral imagery prior for reconstruction","volume":"126","author":"Zhang","year":"2018","journal-title":"Int. J. Comput. Vis."},{"key":"ref_89","doi-asserted-by":"crossref","first-page":"4823","DOI":"10.1109\/TGRS.2019.2893180","article-title":"Learning compact and discriminative stacked autoencoder for hyperspectral image classification","volume":"57","author":"Zhou","year":"2019","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_90","doi-asserted-by":"crossref","first-page":"120","DOI":"10.1016\/j.isprsjprs.2017.11.021","article-title":"A new deep convolutional neural network for fast hyperspectral image classification","volume":"145","author":"Paoletti","year":"2018","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_91","doi-asserted-by":"crossref","unstructured":"Anthony Thomas, V.L., Antoine Cotinat, P.F., and Gilber, M. (2019, January 23\u201325). UAV localization using panoramic thermal cameras. Proceedings of the 12th International Conference on Computer Vision Systems (ICVS), Thessaloniki, Greece.","DOI":"10.1007\/978-3-030-34995-0_69"},{"key":"ref_92","doi-asserted-by":"crossref","first-page":"211","DOI":"10.1007\/s11263-015-0816-y","article-title":"Imagenet large scale visual recognition challenge","volume":"115","author":"Russakovsky","year":"2015","journal-title":"Int. J. Comput. Vis."},{"key":"ref_93","doi-asserted-by":"crossref","unstructured":"Liu, J., Zhang, S., Wang, S., and Metaxas, D.N. (2016). Multispectral deep neural networks for pedestrian detection. arXiv.","DOI":"10.5244\/C.30.73"},{"key":"ref_94","unstructured":"Ren, S., He, K., Girshick, R., and Sun, J. (2015). Faster r-cnn: Towards real-time object detection with region proposal networks. arXiv."},{"key":"ref_95","doi-asserted-by":"crossref","unstructured":"Hwang, S., Park, J., Kim, N., Choi, Y., and So Kweon, I. (2015, January 7\u201312). Multispectral pedestrian detection: Benchmark dataset and baseline. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Boston, MA, USA.","DOI":"10.1109\/CVPR.2015.7298706"},{"key":"ref_96","unstructured":"Lin, M., Chen, Q., and Yan, S. (2013). Network in network. arXiv."},{"key":"ref_97","doi-asserted-by":"crossref","unstructured":"Konig, D., Adam, M., Jarvers, C., Layher, G., Neumann, H., and Teutsch, M. (2017, January 21\u201326). Fully convolutional region proposal networks for multispectral person detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, Honolulu, HI, USA.","DOI":"10.1109\/CVPRW.2017.36"},{"key":"ref_98","doi-asserted-by":"crossref","unstructured":"Bondi, E., Fang, F., Hamilton, M., Kar, D., Dmello, D., Choi, J., Hannaford, R., Iyer, A., Joppa, L., and Tambe, M. (2018, January 2\u20137). Spot poachers in action: Augmenting conservation drones with automatic detection in near real time. Proceedings of the Thirty-Second AAAI Conference on Artificial Intelligence, New Orleans, LA, USA.","DOI":"10.1609\/aaai.v32i1.11414"},{"key":"ref_99","doi-asserted-by":"crossref","first-page":"206","DOI":"10.1016\/j.inffus.2018.06.005","article-title":"Pedestrian detection with unsupervised multispectral feature learning using deep neural networks","volume":"46","author":"Cao","year":"2019","journal-title":"Inf. Fusion"},{"key":"ref_100","doi-asserted-by":"crossref","unstructured":"Kwa\u015bniewska, A., Rumi\u0144ski, J., and Rad, P. (2017, January 17\u201319). Deep features class activation map for thermal face detection and tracking. Proceedings of the 2017 10th International Conference on Human System Interactions (HSI), Ulsan, Korea.","DOI":"10.1109\/HSI.2017.8004993"},{"key":"ref_101","doi-asserted-by":"crossref","unstructured":"Sharif Razavian, A., Azizpour, H., Sullivan, J., and Carlsson, S. (2014, January 24\u201327). CNN features off-the-shelf: An astounding baseline for recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, Columbus, OH, USA.","DOI":"10.1109\/CVPRW.2014.131"},{"key":"ref_102","unstructured":"Yosinski, J., Clune, J., Bengio, Y., and Lipson, H. (2014). How transferable are features in deep neural networks?. arXiv."},{"key":"ref_103","unstructured":"Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., and Wojna, Z. (July, January 1). Rethinking the inception architecture for computer vision. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA."},{"key":"ref_104","doi-asserted-by":"crossref","unstructured":"John, V., Mita, S., Liu, Z., and Qi, B. (2015, January 18\u201322). Pedestrian detection in thermal images using adaptive fuzzy C-means clustering and convolutional neural networks. Proceedings of the 2015 14th IAPR International Conference on Machine Vision Applications (MVA), Tokyo, Japan.","DOI":"10.1109\/MVA.2015.7153177"},{"key":"ref_105","doi-asserted-by":"crossref","first-page":"223","DOI":"10.1016\/j.infrared.2016.08.009","article-title":"Early sinkhole detection using a drone-based thermal camera and image processing","volume":"78","author":"Lee","year":"2016","journal-title":"Infrared Phys. Technol."},{"key":"ref_106","doi-asserted-by":"crossref","unstructured":"Beleznai, C., Steininger, D., Croonen, G., and Broneder, E. (2018, January 19\u201320). Multi-Modal Human Detection from Aerial Views by Fast Shape-Aware Clustering and Classification. Proceedings of the 2018 10th IAPR Workshop on Pattern Recognition in Remote Sensing (PRRS), Beijing, China.","DOI":"10.1109\/PRRS.2018.8486236"},{"key":"ref_107","doi-asserted-by":"crossref","first-page":"2278","DOI":"10.1109\/5.726791","article-title":"Gradient-based learning applied to document recognition","volume":"86","author":"LeCun","year":"1998","journal-title":"Proc. IEEE"},{"key":"ref_108","doi-asserted-by":"crossref","unstructured":"Ulrich, M., Hess, T., Abdulatif, S., and Yang, B. (2018, January 10\u201313). Person recognition based on micro-Doppler and thermal infrared camera fusion for firefighting. Proceedings of the 2018 21st International Conference on Information Fusion (FUSION), Cambridge, UK.","DOI":"10.23919\/ICIF.2018.8455723"},{"key":"ref_109","first-page":"4","article-title":"Robust real-time object detection","volume":"4","author":"Viola","year":"2001","journal-title":"Int. J. Comput. Vis."},{"key":"ref_110","doi-asserted-by":"crossref","unstructured":"Quero, J., Burns, M., Razzaq, M., Nugent, C., and Espinilla, M. (2018). Detection of Falls from Non-Invasive Thermal Vision Sensors Using Convolutional Neural Networks. Proceedings, 2.","DOI":"10.3390\/proceedings2191236"},{"key":"ref_111","doi-asserted-by":"crossref","unstructured":"Bastan, M., Yap, K.H., and Chau, L.P. (2018). Remote detection of idling cars using infrared imaging and deep networks. arXiv.","DOI":"10.1007\/s00521-019-04077-0"},{"key":"ref_112","doi-asserted-by":"crossref","unstructured":"Bastan, M., Yap, K.H., and Chau, L.P. (2018, January 27\u201330). Idling car detection with ConvNets in infrared image sequences. Proceedings of the 2018 IEEE International Symposium on Circuits and Systems (ISCAS), Florence, Italy.","DOI":"10.1109\/ISCAS.2018.8351616"},{"key":"ref_113","doi-asserted-by":"crossref","first-page":"303","DOI":"10.1007\/s11263-009-0275-4","article-title":"The pascal visual object classes (voc) challenge","volume":"88","author":"Everingham","year":"2010","journal-title":"Int. J. Comput. Vis."},{"key":"ref_114","doi-asserted-by":"crossref","first-page":"189","DOI":"10.1016\/j.knosys.2017.07.032","article-title":"Deep convolutional neural networks for thermal infrared object tracking","volume":"134","author":"Liu","year":"2017","journal-title":"Knowl.-Based Syst."},{"key":"ref_115","unstructured":"Felsberg, M., Berg, A., Hager, G., Ahlberg, J., Kristan, M., Matas, J., Leonardis, A., Cehovin, L., Fernandez, G., and Voj\u00edr, T. (2015, January 7\u201313). The thermal infrared visual object tracking VOT-TIR2015 challenge results. Proceedings of the IEEE International Conference on Computer Vision Workshops, Santiago, Chile."},{"key":"ref_116","doi-asserted-by":"crossref","first-page":"6360","DOI":"10.1109\/JSEN.2018.2844252","article-title":"Unobtrusive Sensor-Based Occupancy Facing Direction Detection and Tracking Using Advanced Machine Learning Algorithms","volume":"18","author":"Chen","year":"2018","journal-title":"IEEE Sens. J."},{"key":"ref_117","doi-asserted-by":"crossref","unstructured":"Gao, P., Ma, Y., Song, K., Li, C., Wang, F., and Xiao, L. (2018, January 20\u201324). Large margin structured convolution operator for thermal infrared object tracking. Proceedings of the 2018 24th International Conference on Pattern Recognition (ICPR), Beijing, China.","DOI":"10.1109\/ICPR.2018.8545716"},{"key":"ref_118","unstructured":"Herrmann, C., Ruf, M., and Beyerer, J. (2018, January 16\u201318). CNN-based thermal infrared person detection by domain adaptation. Proceedings of the Autonomous Systems: Sensors, Vehicles, Security, and the Internet of Everything, Orlando, FL, USA."},{"key":"ref_119","doi-asserted-by":"crossref","unstructured":"Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., Fu, C.Y., and Berg, A.C. (2016, January 11\u201314). Ssd: Single shot multibox detector. Proceedings of the European Conference on Computer Vision, Amsterdam, The Netherlands.","DOI":"10.1007\/978-3-319-46448-0_2"},{"key":"ref_120","unstructured":"Patel, S.N., Robertson, T., Kientz, J.A., Reynolds, M.S., and Abowd, G.D. (2007, January 16\u201319). At the flick of a switch: Detecting and classifying unique electrical events on the residential power line (nominated for the best paper award). Proceedings of the International Conference on Ubiquitous Computing, Innsbruck, Austria."},{"key":"ref_121","unstructured":"Lee, H., Pham, P., Largman, Y., and Ng, A.Y. (2009, January 7\u201310). Unsupervised feature learning for audio classification using convolutional deep belief networks. Proceedings of the 22nd International Conference on Neural Information Processing Systems, Vancouver, BC, Canada."},{"key":"ref_122","doi-asserted-by":"crossref","first-page":"82","DOI":"10.1109\/MSP.2012.2205597","article-title":"Deep neural networks for acoustic modeling in speech recognition: The shared views of four research groups","volume":"29","author":"Hinton","year":"2012","journal-title":"IEEE Signal Process. Mag."},{"key":"ref_123","doi-asserted-by":"crossref","unstructured":"Kim, Y., Lee, H., and Provost, E.M. (2013, January 26\u201331). Deep learning for robust feature generation in audiovisual emotion recognition. Proceedings of the 2013 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Vancouver, BC, Canada.","DOI":"10.1109\/ICASSP.2013.6638346"},{"key":"ref_124","doi-asserted-by":"crossref","unstructured":"Deng, L., Li, J., Huang, J.T., Yao, K., Yu, D., Seide, F., Seltzer, M., Zweig, G., He, X., and Williams, J. (2013, January 26\u201331). Recent advances in deep learning for speech research at Microsoft. Proceedings of the 2013 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Vancouver, BC, Canada.","DOI":"10.1109\/ICASSP.2013.6639345"},{"key":"ref_125","doi-asserted-by":"crossref","first-page":"517","DOI":"10.1016\/j.csl.2016.12.004","article-title":"An information fusion framework with multi-channel feature concatenation and multi-perspective system combination for the deep-learning-based robust recognition of microphone array speech","volume":"46","author":"Tu","year":"2017","journal-title":"Comput. Speech Lang."},{"key":"ref_126","doi-asserted-by":"crossref","unstructured":"Piczak, K.J. (2015, January 17\u201320). Environmental sound classification with convolutional neural networks. Proceedings of the 2015 IEEE 25th International Workshop on Machine Learning for Signal Processing (MLSP), Boston, MA, USA.","DOI":"10.1109\/MLSP.2015.7324337"},{"key":"ref_127","doi-asserted-by":"crossref","unstructured":"Cakir, E., Heittola, T., Huttunen, H., and Virtanen, T. (2015, January 12\u201316). Polyphonic sound event detection using multi label deep neural networks. Proceedings of the 2015 International Joint Conference on Neural Networks (IJCNN), Killarney, Ireland.","DOI":"10.1109\/IJCNN.2015.7280624"},{"key":"ref_128","unstructured":"Lane, N.D., Georgiev, P., and Qendro, L. (2015, January 7\u201311). DeepEar: Robust smartphone audio sensing in unconstrained acoustic environments using deep learning. Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing, Osaka, Japan."},{"key":"ref_129","doi-asserted-by":"crossref","first-page":"3964","DOI":"10.1121\/1.4989024","article-title":"Deep learning for unsupervised separation of environmental noise sources","volume":"141","author":"Wilkinson","year":"2017","journal-title":"J. Acoust. Soc. Am."},{"key":"ref_130","doi-asserted-by":"crossref","first-page":"16","DOI":"10.1109\/MSP.2014.2326181","article-title":"Acoustic scene classification: Classifying environments from the sounds they produce","volume":"32","author":"Barchiesi","year":"2015","journal-title":"IEEE Signal Process. Mag."},{"key":"ref_131","doi-asserted-by":"crossref","first-page":"1291","DOI":"10.1109\/TASLP.2017.2690575","article-title":"Convolutional Recurrent Neural Networks for Polyphonic Sound Event Detection","volume":"25","author":"Parascandolo","year":"2017","journal-title":"IEEE\/ACM Trans. Audio Speech Lang. Process."},{"key":"ref_132","unstructured":"Eghbal-Zadeh, H., Lehner, B., Dorfer, M., and Widmer, G. (September, January 28). CP-JKU submissions for DCASE-2016: A hybrid approach using binaural i-vectors and deep convolutional neural networks. Proceedings of the 2017 IEEE 25th European Signal Processing Conference (EUSIPCO), Kos, Greece."},{"key":"ref_133","doi-asserted-by":"crossref","first-page":"279","DOI":"10.1109\/LSP.2017.2657381","article-title":"Deep convolutional neural networks and data augmentation for environmental sound classification","volume":"24","author":"Salamon","year":"2017","journal-title":"IEEE Signal Process. Lett."},{"key":"ref_134","unstructured":"Liu, J., Yu, X., Wan, W., and Li, C. (2009, January 7\u20139). Multi-classification of audio signal based on modified SVM. Proceedings of the IET International Communication Conference on Wireless Mobile and Computing (CCWMC 2009), Shanghai, China."},{"key":"ref_135","doi-asserted-by":"crossref","first-page":"1230","DOI":"10.1109\/TASLP.2017.2690563","article-title":"Unsupervised Feature Learning Based on Deep Models for Environmental Audio Tagging","volume":"25","author":"Xu","year":"2017","journal-title":"IEEE\/ACM Trans. Audio Speech Lang. Process."},{"key":"ref_136","doi-asserted-by":"crossref","unstructured":"Li, J., Dai, W., Metze, F., Qu, S., and Das, S. (2017, January 5\u20139). A comparison of Deep Learning methods for environmental sound detection. Proceedings of the 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), New Orleans, LA, USA.","DOI":"10.1109\/ICASSP.2017.7952131"},{"key":"ref_137","unstructured":"Chowdhury, A.S.K. (2016). Implementation and Performance Evaluation of Acoustic Denoising Algorithms for UAV. [Master\u2019s Thesis, University of Nevada]."},{"key":"ref_138","doi-asserted-by":"crossref","unstructured":"Mezei, J., and Moln\u00e1r, A. (2016, January 12\u201314). Drone sound detection by correlation. Proceedings of the 2016 IEEE 11th International Symposium on Applied Computational Intelligence and Informatics (SACI), Timisoara, Romania.","DOI":"10.1109\/SACI.2016.7507430"},{"key":"ref_139","doi-asserted-by":"crossref","first-page":"60","DOI":"10.2352\/ISSN.2470-1173.2017.10.IMAWM-168","article-title":"Drone detection by acoustic signature identification","volume":"2017","author":"Bernardini","year":"2017","journal-title":"Electron. Imaging"},{"key":"ref_140","unstructured":"Park, S., Shin, S., Kim, Y., Matson, E.T., Lee, K., Kolodzy, P.J., Slater, J.C., Scherreik, M., Sam, M., and Gallagher, J.C. (2015, January 1\u20134). Combination of radar and audio sensors for identification of rotor-type unmanned aerial vehicles (uavs). Proceedings of the 2015 IEEE SENSORS, Busan, Korea."},{"key":"ref_141","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_142","doi-asserted-by":"crossref","unstructured":"Kim, J., Park, C., Ahn, J., Ko, Y., Park, J., and Gallagher, J.C. (2017, January 13\u201315). Real-time UAV sound detection and analysis system. Proceedings of the 2017 IEEE Sensors Applications Symposium (SAS), Glassboro, NJ, USA.","DOI":"10.1109\/SAS.2017.7894058"},{"key":"ref_143","first-page":"43","article-title":"Neural Network based Real-time UAV Detection and Analysis by Sound","volume":"8","author":"Kim","year":"2018","journal-title":"J. Adv. Inf. Technol. Converg."},{"key":"ref_144","doi-asserted-by":"crossref","unstructured":"Salamon, J., Jacoby, C., and Bello, J.P. (2014, January 18\u201319). A dataset and taxonomy for urban sound research. Proceedings of the 22nd ACM International Conference on Multimedia. ACM, Mountain View, CA, USA.","DOI":"10.1145\/2647868.2655045"},{"key":"ref_145","doi-asserted-by":"crossref","unstructured":"Jeon, S., Shin, J.W., Lee, Y.J., Kim, W.H., Kwon, Y., and Yang, H.Y. (September, January 28). Empirical study of drone sound detection in real-life environment with deep neural networks. Proceedings of the 2017 25th European Signal Processing Conference (EUSIPCO), Kos, Greece.","DOI":"10.23919\/EUSIPCO.2017.8081531"},{"key":"ref_146","doi-asserted-by":"crossref","first-page":"273","DOI":"10.1007\/s00521-004-0463-7","article-title":"A review of data fusion models and architectures: Towards engineering guidelines","volume":"14","author":"Esteban","year":"2005","journal-title":"Neural Comput. Appl."},{"key":"ref_147","first-page":"423","article-title":"Multimodal machine learning: A survey and taxonomy","volume":"41","author":"Ahuja","year":"2018","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_148","unstructured":"Ngiam, J., Khosla, A., Kim, M., Nam, J., Lee, H., and Ng, A.Y. (July, January 28). Multimodal deep learning. Proceedings of the 28th International Conference on Machine Learning (ICML-11), Bellevue, WA, USA."},{"key":"ref_149","unstructured":"Sutskever, I., Hinton, G.E., and Taylor, G.W. (2009, January 8\u201310). The recurrent temporal restricted boltzmann machine. Proceedings of the 21st International Conference on Neural Information Processing Systems, Vancouver, BC, Canada."},{"key":"ref_150","doi-asserted-by":"crossref","unstructured":"Patterson, E.K., Gurbuz, S., Tufekci, Z., and Gowdy, J.N. (2002, January 13\u201317). CUAVE: A new audio-visual database for multimodal human-computer interface research. Proceedings of the 2002 IEEE International Conference on Acoustics, Speech, and Signal Processing, Orlando, FL, USA.","DOI":"10.1109\/ICASSP.2002.1006168"},{"key":"ref_151","doi-asserted-by":"crossref","first-page":"198","DOI":"10.1109\/34.982900","article-title":"Extraction of visual features for lipreading","volume":"24","author":"Matthews","year":"2002","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_152","unstructured":"Krizhevsky, A., and Hinton, G. (2019, June 03). Learning Multiple Layers of Features from Tiny Images. Available online: https:\/\/www.cs.toronto.edu\/~kriz\/learning-features-2009-TR.pdf."},{"key":"ref_153","doi-asserted-by":"crossref","first-page":"4308","DOI":"10.1038\/ncomms5308","article-title":"Searching for exotic particles in high-energy physics with deep learning","volume":"5","author":"Baldi","year":"2014","journal-title":"Nat. Commun."},{"key":"ref_154","unstructured":"(2019, June 03). Gender Classification. Available online: https:\/\/www.kaggle.com\/hb20007\/gender-classification."},{"key":"ref_155","doi-asserted-by":"crossref","first-page":"7771","DOI":"10.3390\/s91007771","article-title":"Advances in multi-sensor data fusion: Algorithms and applications","volume":"9","author":"Dong","year":"2009","journal-title":"Sensors"},{"key":"ref_156","doi-asserted-by":"crossref","unstructured":"Patil, U., and Mudengudi, U. (2011, January 3\u20135). Image fusion using hierarchical PCA. Proceedings of the 2011 International Conference on Image Information Processing, Shimla, India.","DOI":"10.1109\/ICIIP.2011.6108966"},{"key":"ref_157","unstructured":"Al-Wassai, F.A., Kalyankar, N., and Al-Zuky, A.A. (2011). The IHS transformations based image fusion. arXiv."},{"key":"ref_158","doi-asserted-by":"crossref","unstructured":"Snoek, C.G., Worring, M., and Smeulders, A.W. (2005, January 6\u201311). Early versus late fusion in semantic video analysis. Proceedings of the 13th Annual ACM International Conference on Multimedia, Singapore.","DOI":"10.1145\/1101149.1101236"},{"key":"ref_159","unstructured":"(2019, June 11). NIST TREC Video Retrieval Evaluation, Available online: http:\/\/www-nlpir.nist.gov\/projects\/trecvid\/."},{"key":"ref_160","unstructured":"Ye, G., Liu, D., Jhuo, I.H., and Chang, S.F. (2012, January 16\u201321). Robust late fusion with rank minimization. Proceedings of the 2012 IEEE Conference on Computer Vision and Pattern Recognition, Providence, RI, USA."},{"key":"ref_161","unstructured":"Nilsback, M.E., and Zisserman, A. (2006, January 17\u201322). A visual vocabulary for flower classification. Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR\u201906), New York, NY, USA."},{"key":"ref_162","unstructured":"Bombini, L., Cerri, P., Medici, P., and Alessandretti, G. (2019, June 11). Radar-Vision Fusion for Vehicle Detection. Available online: http:\/\/www.ce.unipr.it\/people\/bertozzi\/publications\/cr\/wit2006-crf-radar.pdf."},{"key":"ref_163","doi-asserted-by":"crossref","unstructured":"Jovanoska, S., Br\u00f6tje, M., and Koch, W. (2018, January 20\u201322). Multisensor data fusion for UAV detection and tracking. Proceedings of the 2018 19th International Radar Symposium (IRS), Bonn, Germany.","DOI":"10.23919\/IRS.2018.8447971"},{"key":"ref_164","doi-asserted-by":"crossref","first-page":"197","DOI":"10.1016\/j.isprsjprs.2006.09.013","article-title":"Ground target tracking and road map extraction","volume":"61","author":"Koch","year":"2006","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_165","unstructured":"Hengy, S., Laurenzis, M., Schertzer, S., Hommes, A., Kloeppel, F., Shoykhetbrod, A., Geibig, T., Johannes, W., Rassy, O., and Christnacher, F. (2017, January 11\u201314). Multimodal UAV detection: Study of various intrusion scenarios. Proceedings of the Electro-Optical Remote Sensing XI International Society for Optics and Photonics, Warsaw, Poland."},{"key":"ref_166","unstructured":"Laurenzis, M., Hengy, S., Hammer, M., Hommes, A., Johannes, W., Giovanneschi, F., Rassy, O., Bacher, E., Schertzer, S., and Poyet, J.M. (2018, January 16\u201319). An adaptive sensing approach for the detection of small UAV: First investigation of static sensor network and moving sensor platform. Proceedings of the Signal Processing, Sensor\/Information Fusion, and Target Recognition XXVII International Society for Optics and Photonics, Orlando, FL, USA."},{"key":"ref_167","doi-asserted-by":"crossref","unstructured":"Shi, W., Arabadjis, G., Bishop, B., Hill, P., Plasse, R., and Yoder, J. (2011). Detecting, tracking, and identifying airborne threats with netted sensor fence. Sensor Fusion-Foundation and Applications, IntechOpen.","DOI":"10.5772\/17666"},{"key":"ref_168","doi-asserted-by":"crossref","unstructured":"Charvat, G.L., Fenn, A.J., and Perry, B.T. (2012, January 7\u201311). The MIT IAP radar course: Build a small radar system capable of sensing range, Doppler, and synthetic aperture (SAR) imaging. Proceedings of the 2012 IEEE Radar Conference, Atlanta, GA, USA.","DOI":"10.1109\/RADAR.2012.6212126"},{"key":"ref_169","doi-asserted-by":"crossref","unstructured":"Eleni Diamantidou, A.L., Votis, K., and Tzovaras, D. (2019, January 23\u201325). Multimodal Deep Learning Framework for Enhanced Accuracy of UAV Detection. Proceedings of the 12th International Conference on Computer Vision Systems (ICVS), Thessaloniki, Greece.","DOI":"10.1007\/978-3-030-34995-0_70"},{"key":"ref_170","doi-asserted-by":"crossref","first-page":"107","DOI":"10.1007\/s41060-016-0014-1","article-title":"Classifying spatial trajectories using representation learning","volume":"2","author":"Endo","year":"2016","journal-title":"Int. J. Data Sci. Anal."},{"key":"ref_171","unstructured":"Kumaran, S.K., Dogra, D.P., Roy, P.P., and Mitra, A. (2018). Video Trajectory Classification and Anomaly Detection Using Hybrid CNN-VAE. arXiv."},{"key":"ref_172","doi-asserted-by":"crossref","unstructured":"Chen, Y., Aggarwal, P., Choi, J., and Jay, C.C. (2017, January 12\u201315). A deep learning approach to drone monitoring. Proceedings of the 2017 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC), Kuala Lumpur, Malaysia.","DOI":"10.1109\/APSIPA.2017.8282120"},{"key":"ref_173","unstructured":"(2019, October 15). Bounding Box Detection of Drones. Available online: https:\/\/github.com\/creiser\/drone-detection."},{"key":"ref_174","unstructured":"(2019, October 15). MultiDrone Public DataSet. Available online: https:\/\/multidrone.eu\/multidrone-public-dataset\/."},{"key":"ref_175","doi-asserted-by":"crossref","unstructured":"Coluccia, A., Ghenescu, M., Piatrik, T., De Cubber, G., Schumann, A., Sommer, L., Klatte, J., Schuchert, T., Beyerer, J., and Farhadi, M. (September, January 29). Drone-vs-bird detection challenge at IEEE AVSS2017. Proceedings of the 2017 14th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), Lecce, Italy.","DOI":"10.1109\/AVSS.2017.8078464"},{"key":"ref_176","unstructured":"(2019, July 01). 2nd International Workshop on Small-Drone Surveillance, Detection and Counteraction Techqniques (WOSDETC) 2019. Available online: https:\/\/wosdetc2019.wordpress.com\/challenge\/."},{"key":"ref_177","unstructured":"(2019, May 15). Workshop on Vision-Enabled UAV and Counter-UAV Technologies for Surveillance and Security of Critical Infrastructures (UAV4S) 2019. Available online: https:\/\/icvs2019.org\/content\/workshop-vision-enabled-uav-and-counter-uav-technologies-surveillance-and-security-critical."},{"key":"ref_178","doi-asserted-by":"crossref","unstructured":"Chhetri, A., Hilmes, P., Kristjansson, T., Chu, W., Mansour, M., Li, X., and Zhang, X. (2018, January 3\u20137). Multichannel Audio Front-End for Far-Field Automatic Speech Recognition. Proceedings of the 2018 26th European Signal Processing Conference (EUSIPCO), Eternal City, Italy.","DOI":"10.23919\/EUSIPCO.2018.8553149"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/19\/22\/4837\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T13:32:25Z","timestamp":1760189545000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/19\/22\/4837"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,11,6]]},"references-count":178,"journal-issue":{"issue":"22","published-online":{"date-parts":[[2019,11]]}},"alternative-id":["s19224837"],"URL":"https:\/\/doi.org\/10.3390\/s19224837","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,11,6]]}}}