{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,2]],"date-time":"2026-04-02T15:46:26Z","timestamp":1775144786287,"version":"3.50.1"},"reference-count":42,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2022,5,4]],"date-time":"2022-05-04T00:00:00Z","timestamp":1651622400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Natural Science Foundation of China","award":["61901481"],"award-info":[{"award-number":["61901481"]}]},{"name":"National Natural Science Foundation of China","award":["KQTD20190929172704911"],"award-info":[{"award-number":["KQTD20190929172704911"]}]},{"name":"National Natural Science Foundation of China","award":["2021A05"],"award-info":[{"award-number":["2021A05"]}]},{"name":"National Natural Science Foundation of China","award":["2021JJ20056"],"award-info":[{"award-number":["2021JJ20056"]}]},{"name":"Shenzhen Science and Technology Program","award":["61901481"],"award-info":[{"award-number":["61901481"]}]},{"name":"Shenzhen Science and Technology Program","award":["KQTD20190929172704911"],"award-info":[{"award-number":["KQTD20190929172704911"]}]},{"name":"Shenzhen Science and Technology Program","award":["2021A05"],"award-info":[{"award-number":["2021A05"]}]},{"name":"Shenzhen Science and Technology Program","award":["2021JJ20056"],"award-info":[{"award-number":["2021JJ20056"]}]},{"name":"Shenzhen Science and Technology Program","award":["61901481"],"award-info":[{"award-number":["61901481"]}]},{"name":"Shenzhen Science and Technology Program","award":["KQTD20190929172704911"],"award-info":[{"award-number":["KQTD20190929172704911"]}]},{"name":"Shenzhen Science and Technology Program","award":["2021A05"],"award-info":[{"award-number":["2021A05"]}]},{"name":"Shenzhen Science and Technology Program","award":["2021JJ20056"],"award-info":[{"award-number":["2021JJ20056"]}]},{"name":"Hunan Provincial Natural Science Foundation of China","award":["61901481"],"award-info":[{"award-number":["61901481"]}]},{"name":"Hunan Provincial Natural Science Foundation of China","award":["KQTD20190929172704911"],"award-info":[{"award-number":["KQTD20190929172704911"]}]},{"name":"Hunan Provincial Natural Science Foundation of China","award":["2021A05"],"award-info":[{"award-number":["2021A05"]}]},{"name":"Hunan Provincial Natural Science Foundation of China","award":["2021JJ20056"],"award-info":[{"award-number":["2021JJ20056"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>In the past few decades, the demand for reliable and robust systems capable of monitoring unmanned aerial vehicles (UAVs) increased significantly due to the security threats from its wide applications. During UAVs surveillance, birds are a typical confuser target. Therefore, discriminating UAVs from birds is critical for successful non-cooperative UAVs surveillance. Micro-Doppler signature (m-DS) reflects the scattering characteristics of micro-motion targets and has been utilized for many radar automatic target recognition (RATR) tasks. In this paper, the authors deploy local mean decomposition (LMD) to separate the m-DS of the micro-motion parts from the body returns of the UAVs and birds. After the separation, rotating parts will be obtained without the interference of the body components, and the m-DS features can also be revealed more clearly, which is conducive to feature extraction. What is more, there are some problems in using m-DS only for target classification. Firstly, extracting only m-DS features makes incomplete use of information in the spectrogram. Secondly, m-DS can be observed only for metal rotor UAVs, or large UAVs when they are closer to the radar. Lastly, m-DS cannot be observed when the size of the birds is small, or when it is gliding. The authors thus propose an algorithm for RATR of UAVs and interfering targets under a new system of L band staring radar. In this algorithm, to make full use of the information in the spectrogram and supplement the information in exceptional situations, m-DS, movement, and energy aggregation features of the target are extracted from the spectrogram. On the benchmark dataset, the proposed algorithm demonstrates a better performance than the state-of-the-art algorithms. More specifically, the equal error rate (EER) proposed is 2.56% lower than the existing methods, which demonstrates the effectiveness of the proposed algorithm.<\/jats:p>","DOI":"10.3390\/rs14092196","type":"journal-article","created":{"date-parts":[[2022,5,4]],"date-time":"2022-05-04T08:21:25Z","timestamp":1651652485000},"page":"2196","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":16,"title":["Extraction of Micro-Doppler Feature Using LMD Algorithm Combined Supplement Feature for UAVs and Birds Classification"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5094-7647","authenticated-orcid":false,"given":"Ting","family":"Dai","sequence":"first","affiliation":[{"name":"School of Electronics and Communication Engineering, Shenzhen Campus of Sun Yat-sen University, Shenzhen 518107, China"}]},{"given":"Shiyou","family":"Xu","sequence":"additional","affiliation":[{"name":"School of Electronics and Communication Engineering, Shenzhen Campus of Sun Yat-sen University, Shenzhen 518107, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7259-3739","authenticated-orcid":false,"given":"Biao","family":"Tian","sequence":"additional","affiliation":[{"name":"School of Electronics and Communication Engineering, Shenzhen Campus of Sun Yat-sen University, Shenzhen 518107, China"}]},{"given":"Jun","family":"Hu","sequence":"additional","affiliation":[{"name":"School of Electronics and Communication Engineering, Shenzhen Campus of Sun Yat-sen University, Shenzhen 518107, China"}]},{"given":"Yue","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Electronics and Communication Engineering, Shenzhen Campus of Sun Yat-sen University, Shenzhen 518107, China"}]},{"given":"Zengping","family":"Chen","sequence":"additional","affiliation":[{"name":"School of Electronics and Communication Engineering, Shenzhen Campus of Sun Yat-sen University, Shenzhen 518107, China"}]}],"member":"1968","published-online":{"date-parts":[[2022,5,4]]},"reference":[{"key":"ref_1","unstructured":"Chen, V.C., and Ling, H. (2002). Time-Frequency Transforms for Radar Imaging and Signal Analysis, Artech House."},{"key":"ref_2","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_3","doi-asserted-by":"crossref","first-page":"225","DOI":"10.1016\/j.patcog.2017.04.024","article-title":"Regularized 2-D 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_4","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/S0031-3203(98)00052-1","article-title":"Spatio-temporal target identification method of high-range resolution radar","volume":"33","author":"Zhou","year":"2000","journal-title":"Pattern Recognit."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"2109","DOI":"10.1016\/j.patrec.2008.07.006","article-title":"Radar target recognition based on the multi-resolution analysis theory and neural network","volume":"29","author":"Sun","year":"2008","journal-title":"Pattern Recognit. Lett."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"256","DOI":"10.1016\/j.patrec.2012.10.010","article-title":"Radar target recognition based on fuzzy optimal transformation using high-resolution range profile","volume":"34","author":"Zhou","year":"2013","journal-title":"Pattern Recognit. Lett."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"3096","DOI":"10.1016\/j.patcog.2014.03.001","article-title":"Joint tracking and classification based on aerodynamic model and radar cross section","volume":"47","author":"Jiang","year":"2014","journal-title":"Pattern Recognit."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"1743","DOI":"10.1109\/JSEN.2015.2501850","article-title":"Noise robust radar HRRP target recognition based on scatterer matching algorithm","volume":"16","author":"Du","year":"2015","journal-title":"IEEE Sens. J."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"379","DOI":"10.1016\/j.patcog.2016.08.012","article-title":"Radar HRRP target recognition with deep networks","volume":"61","author":"Feng","year":"2017","journal-title":"Pattern Recognit."},{"key":"ref_10","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_11","first-page":"3097","article-title":"CW and pulse\u2013Doppler radar processing based on FPGA for human sensing applications","volume":"51","author":"Wang","year":"2012","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"700","DOI":"10.1016\/j.jfranklin.2008.01.003","article-title":"Micro-Doppler-based target detection and feature extraction in indoor and outdoor environments","volume":"345","author":"Thayaparan","year":"2008","journal-title":"J. Frankl. Inst."},{"key":"ref_13","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_14","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_15","doi-asserted-by":"crossref","first-page":"2164","DOI":"10.1109\/TAES.2014.120792","article-title":"Simulation and analysis of polarimetric radar signatures of human gaits","volume":"50","author":"Park","year":"2014","journal-title":"IEEE Trans. Aerosp. Electron. Syst."},{"key":"ref_16","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_17","doi-asserted-by":"crossref","unstructured":"Bj\u00f6rklund, S., Johansson, T., and Petersson, H. (2012, January 7\u201311). Evaluation of a micro-Doppler classification method on mm-wave data. Proceedings of the 2012 IEEE Radar Conference, Atlanta, GA, USA.","DOI":"10.1109\/RADAR.2012.6212271"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"2304","DOI":"10.1109\/TAES.2014.130082","article-title":"Robust PCA micro-Doppler classification using SVM on embedded systems","volume":"50","author":"Zabalza","year":"2014","journal-title":"IEEE Trans. Aerosp. Electron. Syst."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"417","DOI":"10.1109\/TAES.2014.130762","article-title":"A novel algorithm for radar classification based on Doppler characteristics exploiting orthogonal pseudo-Zernike polynomials","volume":"51","author":"Clemente","year":"2015","journal-title":"IEEE Trans. Aerosp. Electron. Syst."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"3001","DOI":"10.1109\/TGRS.2012.2216885","article-title":"Hierarchical classification of moving vehicles based on empirical mode decomposition of micro-Doppler signatures","volume":"51","author":"Li","year":"2012","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Smith, G.E., Woodbridge, K., and Baker, C.J. (2008, January 2\u20135). Na\u00efve Bayesian radar micro-Doppler recognition. Proceedings of the 2008 International Conference on Radar, Adelaide, Australia.","DOI":"10.1109\/RADAR.2008.4653901"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"1078","DOI":"10.1109\/TAES.2010.5545175","article-title":"Radar micro-Doppler signature classification using dynamic time warping","volume":"46","author":"Smith","year":"2010","journal-title":"IEEE Trans. Aerosp. Electron. Syst."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"607","DOI":"10.1016\/0031-3203(94)90040-X","article-title":"Radar target identification using spatial matched filters","volume":"27","author":"Novak","year":"1994","journal-title":"Pattern Recognit."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"2159","DOI":"10.1016\/j.patcog.2005.02.003","article-title":"Application of feature space trajectory classifier to identification of multi-aspect radar signals","volume":"38","author":"Kim","year":"2005","journal-title":"Pattern Recognit."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Ritchie, M., Fioranelli, F., and Griffiths, H. (2016, January 1\u20136). Monostatic and bistatic radar measurements of birds and micro-drone. Proceedings of the 2016 IEEE Radar Conference (RadarConf), Philadelphia, PA, USA.","DOI":"10.1109\/RADAR.2016.7485181"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Bj\u00f6rklund, S. (2018, January 26\u201328). Target detection and classification of small drones by boosting on radar micro-Doppler. Proceedings of the 2018 15th European Radar Conference (EuRAD), Madrid, Spain.","DOI":"10.23919\/EuRAD.2018.8546569"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Park, S.H., Jung, J.H., Cha, S.B., Kim, S., Youn, S., Eo, I., and Koo, B. (2020, January 19\u201322). In-depth Analysis of the Micro-Doppler Features to Discriminate Drones and Birds. Proceedings of the 2020 International Conference on Electronics, Information, and Communication (ICEIC), Barcelona, Spain.","DOI":"10.1109\/ICEIC49074.2020.9051232"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Farshchian, M., Selesnick, I., and Parekh, A. (2016, January 19\u201322). Bird body and wing-beat radar Doppler signature separation using sparse optimization. Proceedings of the 2016 4th International Workshop on Compressed Sensing Theory and Its Applications to Radar, Sonar and Remote Sensing (CoSeRa), Aachen, Germany.","DOI":"10.1109\/CoSeRa.2016.7745702"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"17396","DOI":"10.1038\/s41598-018-35880-9","article-title":"Radar micro-Doppler signatures of drones and birds at K-band and W-band","volume":"8","author":"Rahman","year":"2018","journal-title":"Sci. Rep."},{"key":"ref_30","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_31","doi-asserted-by":"crossref","unstructured":"Harmanny, R.I.A., De Wit, J.J.M., and Cabic, G.P. (2014, January 8\u201310). Radar micro-Doppler feature extraction using the spectrogram and the cepstrogram. Proceedings of the 2014 11th European Radar Conference, Rome, Italy.","DOI":"10.1109\/EuRAD.2014.6991233"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"469","DOI":"10.1017\/S1759078715001002","article-title":"Radar micro-Doppler mini-UAV classification using spectrograms and cepstrograms","volume":"7","author":"Harmanny","year":"2015","journal-title":"Int. J. Microw. Wirel. Technol."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"443","DOI":"10.1098\/rsif.2005.0058","article-title":"The local mean decomposition and its application to EEG perception data","volume":"2","author":"Smith","year":"2005","journal-title":"J. R. Soc. Interface"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"867","DOI":"10.1016\/j.neucom.2010.07.030","article-title":"The complex local mean decomposition","volume":"74","author":"Park","year":"2011","journal-title":"Neurocomputing"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"1285","DOI":"10.1109\/TGRS.2013.2249588","article-title":"Micro-Doppler analysis and separation based on complex local mean decomposition for aircraft with fast-rotating parts in ISAR imaging","volume":"52","author":"Yuan","year":"2013","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_36","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_37","doi-asserted-by":"crossref","unstructured":"Srigrarom, S., Chew, K.H., Da Lee, D.M., and Ratsamee, P. (2020, January 23\u201326). Drone versus bird flights: Classification by trajectories characterization. Proceedings of the 2020 59th Annual Conference of the Society of Instrument and Control Engineers of Japan (SICE), Chiang Mai, Thailand.","DOI":"10.23919\/SICE48898.2020.9240313"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Zhang, X., Mehta, V., Bolic, M., and Mantegh, I. (2020, January 11\u201314). Hybrid AI-enabled Method for UAS and Bird Detection and Classification. Proceedings of the 2020 IEEE International Conference on Systems, Man, and Cybernetics (SMC), Toronto, ON, Canada.","DOI":"10.1109\/SMC42975.2020.9282965"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"7182","DOI":"10.1109\/TGRS.2019.2912019","article-title":"Sparse recovery on intrinsic mode functions for the micro-Doppler parameters estimation of small UAVs","volume":"57","author":"Zhao","year":"2019","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"929","DOI":"10.1109\/TIM.2019.2905751","article-title":"The extraction of micro-Doppler signal with EMD algorithm for radar-based small UAVs\u2019 detection","volume":"69","author":"Zhao","year":"2019","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"1933","DOI":"10.1109\/LGRS.2015.2439393","article-title":"Classification of unarmed\/armed personnel using the NetRAD multistatic radar for micro-Doppler and singular value decomposition features","volume":"12","author":"Fioranelli","year":"2015","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Dale, H., Baker, C., Antoniou, M., and Jahangir, M. (May, January 27). An Initial Investigation into Using Convolutional Neural Networks for Classification of Drones. Proceedings of the 2020 IEEE International Radar Conference (RADAR), Washington, DC, USA.","DOI":"10.1109\/RADAR42522.2020.9114745"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/9\/2196\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T23:05:56Z","timestamp":1760137556000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/9\/2196"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,5,4]]},"references-count":42,"journal-issue":{"issue":"9","published-online":{"date-parts":[[2022,5]]}},"alternative-id":["rs14092196"],"URL":"https:\/\/doi.org\/10.3390\/rs14092196","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,5,4]]}}}