Skip to main content Accessibility help
×
Home
Hostname: page-component-7f7b94f6bd-rpk4r Total loading time: 0.421 Render date: 2022-06-30T06:34:42.436Z Has data issue: true Feature Flags: { "shouldUseShareProductTool": true, "shouldUseHypothesis": true, "isUnsiloEnabled": true, "useRatesEcommerce": false, "useNewApi": true } hasContentIssue true

Detection and recognition of UA targets with multiple sensors

Published online by Cambridge University Press:  13 May 2022

W.S. Chen*
Affiliation:
China Academy of Civil Aviation Science and Technology, Beijing100028, China
X.L. Chen
Affiliation:
Naval Aviation University, Yantai264001, China
J. Liu
Affiliation:
Beihang University, Beijing100191, China
Q.B. Wang
Affiliation:
China Academy of Civil Aviation Science and Technology, Beijing100028, China
X.F. Lu
Affiliation:
China Academy of Civil Aviation Science and Technology, Beijing100028, China
Y.F. Huang
Affiliation:
China Academy of Civil Aviation Science and Technology, Beijing100028, China
*
*Corresponding author. Email: wishchen@buaa.edu.cn

Abstract

Modern low-altitude unmanned aircraft (UA) detection and surveillance systems mostly adopt the multi-sensor fusion technology scheme of radar, visible light, infrared, acoustic and radio detection. Firstly, this paper summarises the latest research progress of UA and bird target detection and recognition technology based on radar, and provides an effective way of detection and recognition from the aspects of echo modeling and micro motion characteristic cognition, manoeuver feature enhancement and extraction, motion trajectory difference, deep learning intelligent classification, etc. Furthermore, this paper also analyses the target feature extraction and recognition algorithms represented by deep learning for other kinds of sensor data. Finally, after a comparison of the detection ability of various detection technologies, a technical scheme for low-altitude UA surveillance system based on four types of sensors is proposed, with a detailed description of its main performance indicators.

Type
Survey Paper
Copyright
© The Author(s), 2022. Published by Cambridge University Press on behalf of Royal Aeronautical Society

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Chen, W., Wan, X. and Li, J. Detection technology of non-cooperative UAV targets in airport clearance area, J Civil Aviat, 2018, 2, (5), pp 5457.Google Scholar
Jin, W. and Shang, Y. The safety supervision of unmanned aircraft systems in China, Sci Technol Rev, 2019, 37, (14), pp 6677.Google Scholar
Luo, J. and Yang, Y. An overview of target detection methods based on data fusion, Control Decis, 2020, 35, (1), pp 115.Google Scholar
Li, X., Zha, Y., Zhang, T., et al. Survey of visual object tracking algorithms based on deep learning, J Image Graph, 2019, 24, (12), pp 20572080.Google Scholar
Chen, W., Liu, J., Chen, X., et al. Non-cooperative UAV target recognition in low-altitude airspace based on motion model, J Beijing Univ Aeronaut Astronaut, 2019, 45, (4), pp 687694.Google Scholar
Chen, W. and Li, J. Review on development and applications of avian radar technology, Modern Radar, 2017, 39, (2), pp 717.Google Scholar
Li, S., Sun, R., Sun, J., et al. A study on the architecture of radar ATR based on deep learning, Modern Radar, 2019, 41, (11), pp 5761+84.Google Scholar
Luo, Q. Small moving target detection using sparse clutter modeling, Modern Radar, 2016, 38, (2), pp 4346+83.Google Scholar
Liu, Y., Yi, J., Wan, X., et al. Time varying clutter suppression in CP OFDM based passive radar for slowly moving targets detection, IEEE Sens J Early Access. doi: 10.1109/JSEN.2020.2986717.CrossRefGoogle Scholar
Yi, Y., Wan, X., Yi, J., et al. Polarization diversity technology research in passive radar based on subcarrier processing, IEEE Sens J, 2019, 19, (5), pp 17101719. doi: 10.1109/JSEN.2018.2881226.CrossRefGoogle Scholar
Dan, Y., Yi, J., Wan, X., et al. LTE-based passive radar for drone detection and its experimental results, J Eng, 2019, 2019, (20), pp 69106913. doi: 10.1049/joe.2019.0583.CrossRefGoogle Scholar
Anderson, R. Avian Radar Systems [EB/OL]. Panama City, Florida: [s. n.], 2007 [2020-08-31]. http://www.detect-inc.com/downloads Google Scholar
Weber, P., Nohara, T.J. and Gauthreaux, S.A. Affordable, real-time, 3-D avian radar networks for centralized North American bird advisory systems, Proceedings of the Bird Strike Committee-USA/Canada, 2005.Google Scholar
AC No: 150/5220-25 FAA Advisory Circular on Airport Avian Radar Systems, 2010.Google Scholar
Hoffmann, F., Ritchie, M., Fioranelli, F., et al. Micro-Doppler based detection and tracking of UAVs with multistatic radar, 2016 IEEE Radar Conference (RadarConf), 2–6 May 2016, Philadelphia, PA, USA. doi: 10.1109/RADAR.2016.7485236.CrossRefGoogle Scholar
Zhang, Q., Hu, J., Luo, Y., et al. Research progresses in radar feature extraction, imaging, and recognition of target with micro-motions, J Radars, 2018, 7, (5), pp 531547.Google Scholar
Li, X., Huang, Y., Yin, K., et al. Micro-motion signature and recognition of battlefield targets in airborne radar, Modern Radar, 2017, 39, (2), pp 5660.Google Scholar
Ritchie, M., Fioranelli, F., Griffiths, H., et al. Micro-drone RCS analysis, 2015 IEEE Radar Conference. IEEE Press, Arlington, VA, USA, 2015, pp 452456.CrossRefGoogle Scholar
Chen, V.C. The Micro-Doppler Effect in Radar. Artech House, Norwood, 2011.Google Scholar
Jahangir, M. and Baker, C.J. Extended dwell Doppler characteristics of birds and micro-UAS at l-band, 2017 18th International Radar Symposium (IRS), Prague, 2017, pp 110, doi: 10.23919/IRS.2017.8008144.CrossRefGoogle Scholar
Singh, A.K. and Kim, Y. Automatic measurement of blade length and rotation rate of drone using w-band micro-doppler radar, IEEE Sens J, 2018, 18, (5), pp 18951902. doi: 10.1109/JSEN.2017.2785335.CrossRefGoogle Scholar
Song, C., Zhou, L., Wu, Y. and Ding, C. An estimation method of rotation frequency of unmanned aerial vehicle based on auto-correlation and cepstrum, J Electron Inform Technol, 2019, 41, (2), pp 255261. doi: 10.11999/JEIT180399.Google Scholar
Rahman, S. and Robertson, D.A. Radar micro-Doppler signatures of drones and birds at K-band and W-band, Sci Rep, 2018, 8, (1), pp 111. doi: 10.1038/s41598-018-35880-9.CrossRefGoogle ScholarPubMed
De Wit, J.J.M., Harmanny, R.I.A. and Premel-Cabic, G. Micro-Doppler analysis of small UAVs, 2012 9th European Radar Conference, IEEE Press, Amsterdam, The Netherlands, 2012, pp 210–213.Google Scholar
Harmanny, R., De Wit, J. and Cabitc, G.P. Radar micro-Doppler feature extraction using the spectrogram and the cepstrogram, 2014 11th European Radar Conference. IEEE Press, Cincinnati, OH, USA, 2014, pp 165–168.CrossRefGoogle Scholar
Molchanov, P., Egiazarian, K., Astola, J.T., et al. Classification of small UAVs and birds by micro-Doppler signatures, Int J Microwave Wirel Technol, 2013, 6, (3–4), pp 435444.CrossRefGoogle Scholar
Ma, J., Dong, Y., Li, Y., et al. Multi-rotor UAV’s micro-Doppler characteristic analysis and feature extraction, J Univ Chin Acad Sci, 2019, 36, (2), pp 235243.Google Scholar
De Wit, J.J.M., Harmanny, R.I.A. and Molchanov, P. Radar micro-Doppler feature extraction using the singular value decomposition, 2014 International Radar Conference. IEEE Press, Lille, France, 2014, pp 1–6.CrossRefGoogle Scholar
Fuhrmann, L., Biallawons, O., Klare, J., et al. Micro-Doppler analysis and classification of UAVs at Ka band, 2017 18th International Radar Symposium (IRS). IEEE Press, Prague, Czech Republic, 2017, pp 19.CrossRefGoogle Scholar
Song, C., Zhou, L., Wu, Y., et al. An estimation method of rotation frequency of unmanned aerial vehicle based on auto-correlation and cepstrum, J Electron Inform Technol, 2019, 41, (2), pp 255261.Google Scholar
Ren, J. and Jiang, X. Regularized 2D complex-log spectral analysis and subspace reliability analysis of micro-Doppler signature for UAV detection, Pattern Recognit, 2017, 69, pp 225237.CrossRefGoogle Scholar
Oh, B.S., Guo, X., Wan, F.Y., et al. Micro-Doppler mini-UAV classification using empirical-mode decomposition features, IEEE Geosci Rem Sens Lett, 2017, 15, pp 227231.CrossRefGoogle Scholar
Ma, X., Oh, B.S., Sun, L., et al. EMD-based entropy features for micro-Doppler mini-UAV classification, 2018 24th International Conference on Pattern Recognition (ICPR), IEEE Press, Beijing, 2018, pp 12951300.CrossRefGoogle Scholar
Sun, Y., Fu, H., Abeywickrama, S., et al. Drone classification and localization using micro-doppler signature with low-frequency signal, 2018 IEEE International Conference on Communication Systems (ICCS). IEEE Press, Chengdu, 2018, pp 413417.CrossRefGoogle Scholar
Fioranelli, F., Ritchie, M., Griffiths, H., et al. Classification of loaded/unloaded micro-drones using multistatic radar, Electron Lett, 2015, 51, pp 18131815.CrossRefGoogle Scholar
Hoffmann, F., Ritchie, M., Fioranelli, F., et al. Micro-Doppler based detection and tracking of UAVs with multistatic radar, 2016 IEEE Radar Conference (RadarConf). IEEE Press, Philadelphia, PA, USA, 2016, pp 16.CrossRefGoogle Scholar
Zhang, P., Yang, L., Chen, G., et al. Classification of drones based on micro-Doppler signatures with dual-band radar sensors, 2017 Progress in Electromagnetics Research Symposium-Fall (PIERS-FALL). IEEE Press, Singapore, 2017, pp 638643.CrossRefGoogle Scholar
Zhang, P., Li, G., Huo, C., et al. Classification of drones based on micro-doppler radar signatures using dual radar sensors, J Radars, 2018, 7, (5), pp 557564.Google Scholar
Liu, Y., Yi, J., Wan, X., et al. Experimental research on micro-doppler effect of multi-rotor drone with digital television based passive radar, J Radars, 2018, 7, (5), 585592.Google Scholar
Singh, A.K. and Kim, Y. Automatic measurement of blade length and rotation rate of drone using w-band micro-doppler radar, IEEE Sens J, 2018, 18, (5), pp 18951902. doi: 10.1109/JSEN.2017.2785335.CrossRefGoogle Scholar
Song, C., Zhou, L., Wu, Y. and Ding, C. An estimation method of rotation frequency of unmanned aerial vehicle based on auto-correlation and cepstrum, J Electron Inform Technol, 2019, 41, (2), pp 255261.Google Scholar
Rahman, S. and Robertson, D.A. Radar micro-Doppler signatures of drones and birds at K-band and W-band, Sci Rep, 2018, 8, (1), pp 111. doi: 10.1038/s41598-018-35880-9.CrossRefGoogle Scholar
Chen, X., Guan, J., Liu, N., et al. Maneuvering target detection via Radon-fractional Fourier transform-based long-time coherent integration, IEEE Trans Sig Process, 2014, 62, (4), pp 939953. doi: 10.1109/TSP.2013.2297682.CrossRefGoogle Scholar
Chen, X., Guan, J., Chen, W., et al. Sparse long-time coherent integration-based detection method for radar low-observable manoeuvring target, IET Radar Sonar Navigat, 2020, 14, (4), pp 538546. doi: 10.1049/iet-rsn.2019.0313.CrossRefGoogle Scholar
Chen, X., Guan, J., Wang, G. et al. Fast and refined processing of radar maneuvering target based on hierarchical detection via sparse fractional representation, IEEE Access, 2019, 7, pp 149878149889. doi: 10.1109/ACCESS.2019.2947169.CrossRefGoogle Scholar
Patel, J.S., Fioranelli, F. and Anderson, D. Review of radar classification and RCS characterization techniques for small UAVs or drones, IET Radar Sonar Navigat, 2018, 12, pp 911919.CrossRefGoogle Scholar
Chen, W., Liu, J. and Li, J. Classification of UAV and bird target in low-altitude airspace with surveillance radar data, Aeronaut J, 2019, 123, pp 191211.CrossRefGoogle Scholar
Messina, M. and Pinelli, G. Classification of drones with a surveillance radar signal, 12th International Conference on Computer Vision Systems (ICVS). Springer, Thessaloniki, Greece, 2019, pp 110.CrossRefGoogle Scholar
Torvik, B., Olsen, K.E. and Griffiths, H. Classification of birds and UAVs based on radar polarimetry, IEEE Geosci Rem Sens Lett, 2016, 13, pp 13051309.CrossRefGoogle Scholar
Kim, B.K., Kang, H.S. and Park, S.O. Drone classification using convolutional neural networks with merged Doppler images, IEEE Geosci. Rem. Sens. Lett, 2016, 14, pp 3842.CrossRefGoogle Scholar
Mendis, G.J., Randeny, T., Wei, J., et al. Deep learning based doppler radar for micro UAS detection and classification, MILCOM 2016-2016 IEEE Military Communications Conference. IEEE Press, Baltimore, MD, USA, 2016, pp 924–929.CrossRefGoogle Scholar
Wang, L., Tang, J. and Liao, Q. A study on radar target detection based on deep neural networks. IEEE Sens Lett, 2019, doi: 10.1109/LSENS.2019.2896072.CrossRefGoogle Scholar
Samaras, S., Diamantidou, E., Ataloglou, D., et al. Deep learning on multi sensor data for counter UAV applications--A systematic review, Sensors, 2019, 19, (22), pp 48374871.CrossRefGoogle ScholarPubMed
Regev, N., Yoffe, I. and Wulich, D. Classification of single and multi propelled miniature drones using multilayer perceptron artificial neural network, International Conference on Radar Systems (Radar 2017). IET Press, Belfast, UK, 2017, pp 15.Google Scholar
Habermann, D., Dranka, E, Caceres, Y, et al. Drones and helicopters classification using point clouds features from radar, 2018 IEEE Radar Conference (RadarConf18). IEEE Press, Oklahoma City, OK, USA, 2018, pp 246251.CrossRefGoogle Scholar
Mohajerin, N., Histon, J., Dizaji, R., et al. Feature extraction and radar track classification for detecting UAVs in civillian airspace, 2014 IEEE Radar Conference. IEEE Press, Cincinnati, OH, USA, 2014, pp 674–679.CrossRefGoogle Scholar
Krizhevsky, A., Sutskever, I. and Hinton, G.E. Imagenet classification with deep convolutional neural networks, Adv Neural Inf Process Syst 2012, 25, pp 10971105.Google Scholar
Saqib, M., Khan, S.D., Sharma, N., et al. A study on detecting drones using deep convolutional neural networks, 2017 14th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS). IEEE Press, Lecce, Italy, 2017, pp 15.CrossRefGoogle Scholar
Simonyan, K. and Zisserman, A. Very deep convolutional networks for large-scale image recognition. arXiv 2014, arXiv:1409.1556.Google Scholar
Zeiler, M.D. and Fergus, R. Visualizing and understanding convolutional networks. Proceedings of the European Conference on Computer Vision, Zurich, Switzerland, 6–12 September 2014, pp. 818833.CrossRefGoogle Scholar
Craye, C. and Ardjoune, S. Spatio-temporal semantic segmentation for drone detection, 2019 16th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS). IEEE Press, Taipei, China, 2019, pp 15.CrossRefGoogle Scholar
Vasileios Magoulianitis, D.A., Anastasios Dimou, D.Z., Daras, P. Does deep super-resolution enhance UAV detection, 2019 16th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS). IEEE Press, Taipei, China, 2019, pp 16.CrossRefGoogle Scholar
Aker, C. and Kalkan, S. Using deep networks for drone detection, 2017 14th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS). IEEE Press, Lecce, Italy, 2017, pp 16.CrossRefGoogle Scholar
Redmon, J., Divvala, S., Girshick, R. and Farhadi, A. You only look once: Unified, real-time object detection, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA, 26 June-1 July 2016, pp 779788.CrossRefGoogle Scholar
Rozantsev, A., Lepetit, V. and Fua, P. Detecting flying objects using a single moving camera, IEEE Trans Pattern Anal Mach Intell, 2016, 39, (5), pp 879892.CrossRefGoogle ScholarPubMed
Gökçe, F., Üçoluk, G., Sahin, E., et al. Vision-based detection and distance estimation of micro-unmanned aerial vehicles, Sensors, 2015, 15, (9), pp 2380523846.CrossRefGoogle ScholarPubMed
Anthony Thomas, V.L., Antoine Cotinat, P.F., Gilber, M. UAV localization using panoramic thermal cameras, Proceedings of the 12th International Conference on Computer Vision Systems (ICVS), Thessaloniki, Greece, 23–25 September 2019.CrossRefGoogle Scholar
Liu, J., Zhang, S, Wang, S., et al. Multispectral deep neural networks for pedestrian detection, British Machine Vision Conference 2016. British Machine Vision Association, York, UK, 2016, pp 113.CrossRefGoogle Scholar
Cao, Y., Guan, D., Huang, W., et al. Pedestrian detection with unsupervised multispectral feature learning using deep neural networks, Inform Fusion, 2019, 46, pp 206217.CrossRefGoogle Scholar
John, V., Mita, S., Liu, Z., et al. Pedestrian detection in thermal images using adaptive fuzzy C-means clustering and convolutional neural networks, 2015 14th IAPR International Conference on Machine Vision Applications (MVA), IEEE Press, Tokyo, Japan, 2015, pp 246249.CrossRefGoogle Scholar
Ulrich, M., Hess, T., Abdulatif, S., et al. Person recognition based on micro-Doppler and thermal infrared camera fusion for firefighting, 2018 21st International Conference on Information Fusion (FUSION). IEEE Press, Cambridge, UK, 2018, pp 919926.CrossRefGoogle Scholar
Viola, P. and Jones, M. Robust real-time object detection, Int J Comput Vis, 2001, 4, p 4.Google Scholar
Liu, Q., Lu, X., He, Z., et al. Deep convolutional neural networks for thermal infrared object tracking, Knowl-Based Syst, 2017, 134, pp 189198.CrossRefGoogle Scholar
Microflown AviSa. Sky sentry: Acoustic MicroDrone localization: system in an urban environment 2015. Available online: https://microflown-avisa.com/wp-content/uploads/2015/09/PRESSRELEASE-ACOUSTICDRONELOCALIZATIONSYSTEM150715.pdf Google Scholar
Lee, H., Pham, P., Largman, Y., et al. Unsupervised feature learning for audio classification using convolutional deep belief networks, 22nd International Conference on Neural Information Processing Systems. IEEE Press, Vancouver, BC, Canada, 2009, pp 1096–1104.Google Scholar
Piczak, K.J. Environmental sound classification with convolutional neural networks, 2015 IEEE 25th International Workshop on Machine Learning for Signal Processing (MLSP). IEEE Press, Boston, MA, USA, 2015, pp 16.CrossRefGoogle Scholar
Lane, N.D., Georgiev, P. and Qendro, L. DeepEar: robust smartphone audio sensing in unconstrained acoustic environments using deep learning, 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing. IEEE Press, Osaka, Japan, 2015, pp 283–294.CrossRefGoogle Scholar
Wilkinson, B., Ellison, C., Nykaza, E.T., et al. Deep learning for unsupervised separation of environmental noise sources, J Acoust Soc Amer, 2017, 141, pp 39643964.CrossRefGoogle Scholar
Park, S., Shin, S., Kim, Y., et al. Combination of radar and audio sensors for identification of rotor-type unmanned aerial vehicles (UAVs), 2015 IEEE SENSORS. IEEE Press, Busan, Korea, 2015, pp 1–4.Google ScholarPubMed
Liu, H., Wei, Z., Chen, Y., et al. Drone detection based on an audio-assisted camera array, 2017 IEEE Third International Conference on Multimedia Big Data (BigMM). IEEE Press, Laguna Hills, CA, USA, 2017, pp 402406.CrossRefGoogle Scholar
Kim, J., Park, C., Ahn, J., et al. Real-time UAV sound detection and analysis system, 2017 IEEE Sensors Applications Symposium (SAS). IEEE Press, Glassboro, NJ, USA, 2017, pp 15.CrossRefGoogle Scholar
Kim, J. and Kim, D. Neural network based real-time UAV detection and analysis by sound, J Adv Inform Technol Converg, 2018, 8, (1), pp 4352.CrossRefGoogle Scholar
Jeon, S., Shin, J.W., Lee, Y.J., et al. Empirical study of drone sound detection in real-life environment with deep neural networks, 2017 25th European Signal Processing Conference (EUSIPCO). IEEE Press, Kos, Greece, 2017, pp 1858–1862.CrossRefGoogle Scholar
Ma, C., Zhang, J. and Bao, M. UAV acoustic detection based on non-parametric fusion of spatial-frequency characteristics of sound field, J Sig Process, 2019, 35, (9), pp 15901598.Google Scholar
Wang, W., An, T. and Ou, J. Research on audio detection and recognition of UAV, Tech Acoust, 2018, 37, (1), pp 8993.Google Scholar
Zhu, Y., Tu, D., Li, X. Research on the field test system of radio monitoring and direction finding facilities, China Radio, 2020, 2020, (1), pp 3841.Google Scholar
Wang, Z., Zhou, X. and Xu, C. Research on detection and control method of civilian UAV based on radio technology in prison environment, Comput Sci Appl, 2018, 8, (9), pp 14071415.Google Scholar
O’Shea, T., Corgan, J. and Clancy, T.C. Convolutional radio modulation recognition networks, International Conference on Engineering Applications of Neural Networks. Springer, Aberdeen, UK, 2016, pp 213–226.CrossRefGoogle Scholar
Karra, K., Kuzdeba, S. and Pertersen, J. Modulation recognition using hierarchical deep neural networks, 2017 IEEE International Symposium on Dynamic Spectrum Access Networks (DySPAN). IEEE Press, Piscataway, New Jersey, USA, 2017, pp 13.CrossRefGoogle Scholar
West, N. and O’Shea, T. Deep architectures for modulation recognition, 2017 IEEE International Symposium on Dynamic Spectrum Access Networks (DySPAN). IEEE Press, Piscataway, New Jersey, USA, 2017, pp 16.CrossRefGoogle Scholar
Arumugam, K., Kadampot, I., Tahmasbi, M., et al. Modulation recognition using side information and hybrid learning, 2017 IEEE International Symposium on Dynamic Spectrum Access Networks (DySPAN). IEEE Press, Piscataway, New Jersey, USA, 2017, pp 12.Google Scholar
Schmidt, M., Block, D. and Meier, U. Wireless interference identification with convolutional neural networks, 2017 IEEE 15th International Conference on Industrial Informatics (INDIN). IEEE Press, Emden, Germany, 2017, pp 180185.CrossRefGoogle Scholar
Baltrušaitis, T., Ahuja, C. and Morency, L.P. Multimodal machine learning: A survey and taxonomy, IEEE Trans Pattern Anal Mach Intell 2018, 41, pp 423443.CrossRefGoogle Scholar
Endo, Y., Toda, H., Nishida, K. and Ikedo, J. Classifying spatial trajectories using representation learning, Int J Data Sci Anal, 2016, 2, pp 107117.CrossRefGoogle Scholar
Bounding Box Detection of Drones. 2017. Available online: https://github.com/creiser/drone-detection/ Google Scholar
MultiDrone Public DataSet. 2018. Available online: https://multidrone.eu/multidrone-public-dataset/ Google Scholar
Yuan, B.Q., Wang, Y.S and Zheng, L.G. A survey of deep learning applied on radio signal modulation recognition, Appl Electron Tech, 2019, 45, (5), pp 14.Google Scholar
Zhang, Y. and Hu, S. Research on radar/visible light surveillance and tracking methods of target in low-altitude airspace, Comput Eng Appl, 2018, 54, (6), pp 234240.Google Scholar
Jovanoska, S., Brötje, M. and Koch, W. Multisensor data fusion for UAV detection and tracking, Proceedings of the 2018 19th International Radar Symposium (IRS), Bonn, Germany, 20–22 June 2018, pp. 110.CrossRefGoogle Scholar
Hengy, S., Laurenzis, M., Schertzer, S., Hommes, A., Kloeppel, F., Shoykhetbrod, A., Geibig, T., Johannes, W., Rassy, O. and Christnacher, F. Multimodal UAV detection: Study of various intrusion scenarios. In Proceedings of the Electro-Optical Remote Sensing XI International Society for Optics and Photonics, Warsaw, Poland, 11–14 September 2017, Volume 10434, p. 104340P.Google Scholar
Laurenzis, M., Hengy, S., Hammer, M., Hommes, A., Johannes, W., Giovanneschi, F., Rassy, O., Bacher, E., Schertzer, S. and Poyet, J.M. 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, 16–19 April 2018, Volume 10646, p 106460S.Google Scholar
Supplementary material: File

Chen et al. supplementary material

Chen et al. supplementary material

Download Chen et al. supplementary material(File)
File 8 MB

Save article to Kindle

To save this article to your Kindle, first ensure coreplatform@cambridge.org is added to your Approved Personal Document E-mail List under your Personal Document Settings on the Manage Your Content and Devices page of your Amazon account. Then enter the ‘name’ part of your Kindle email address below. Find out more about saving to your Kindle.

Note you can select to save to either the @free.kindle.com or @kindle.com variations. ‘@free.kindle.com’ emails are free but can only be saved to your device when it is connected to wi-fi. ‘@kindle.com’ emails can be delivered even when you are not connected to wi-fi, but note that service fees apply.

Find out more about the Kindle Personal Document Service.

Detection and recognition of UA targets with multiple sensors
Available formats
×

Save article to Dropbox

To save this article to your Dropbox account, please select one or more formats and confirm that you agree to abide by our usage policies. If this is the first time you used this feature, you will be asked to authorise Cambridge Core to connect with your Dropbox account. Find out more about saving content to Dropbox.

Detection and recognition of UA targets with multiple sensors
Available formats
×

Save article to Google Drive

To save this article to your Google Drive account, please select one or more formats and confirm that you agree to abide by our usage policies. If this is the first time you used this feature, you will be asked to authorise Cambridge Core to connect with your Google Drive account. Find out more about saving content to Google Drive.

Detection and recognition of UA targets with multiple sensors
Available formats
×
×

Reply to: Submit a response

Please enter your response.

Your details

Please enter a valid email address.

Conflicting interests

Do you have any conflicting interests? *