{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,11]],"date-time":"2026-03-11T21:24:48Z","timestamp":1773264288821,"version":"3.50.1"},"reference-count":31,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2022,2,24]],"date-time":"2022-02-24T00:00:00Z","timestamp":1645660800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["U1933135, 61931021"],"award-info":[{"award-number":["U1933135, 61931021"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Aiming at the difficult problem of the classification between flying bird and rotary-wing drone by radar, a micro-motion feature classification method is proposed in this paper. Using K-band frequency modulated continuous wave (FMCW) radar, data acquisition of five types of rotor drones (SJRC S70 W, DJI Mavic Air 2, DJI Inspire 2, hexacopter, and single-propeller fixed-wing drone) and flying birds is carried out under indoor and outdoor scenes. Then, the feature extraction and parameterization of the corresponding micro-Doppler (m-D) signal are performed using time-frequency (T-F) analysis. In order to increase the number of effective datasets and enhance m-D features, the data augmentation method is designed by setting the amplitude scope displayed in T-F graph and adopting feature fusion of the range-time (modulation periods) graph and T-F graph. A multi-scale convolutional neural network (CNN) is employed and modified, which can extract both the global and local information of the target\u2019s m-D features and reduce the parameter calculation burden. Validation with the measured dataset of different targets using FMCW radar shows that the average correct classification accuracy of drones and flying birds for short and long range experiments of the proposed algorithm is 9.4% and 4.6% higher than the Alexnet- and VGG16-based CNN methods, respectively.<\/jats:p>","DOI":"10.3390\/rs14051107","type":"journal-article","created":{"date-parts":[[2022,2,24]],"date-time":"2022-02-24T21:11:07Z","timestamp":1645737067000},"page":"1107","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":33,"title":["Micro-Motion Classification of Flying Bird and Rotor Drones via Data Augmentation and Modified Multi-Scale CNN"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1040-1655","authenticated-orcid":false,"given":"Xiaolong","family":"Chen","sequence":"first","affiliation":[{"name":"Marine Target Detection Research Group, Naval Aviation University, Yantai 264001, China"}]},{"given":"Hai","family":"Zhang","sequence":"additional","affiliation":[{"name":"Marine Target Detection Research Group, Naval Aviation University, Yantai 264001, China"}]},{"given":"Jie","family":"Song","sequence":"additional","affiliation":[{"name":"Marine Target Detection Research Group, Naval Aviation University, Yantai 264001, China"}]},{"given":"Jian","family":"Guan","sequence":"additional","affiliation":[{"name":"Marine Target Detection Research Group, Naval Aviation University, Yantai 264001, China"}]},{"given":"Jiefang","family":"Li","sequence":"additional","affiliation":[{"name":"School of Communication and Electronic Engineering, East China Normal University, Shanghai 200050, China"}]},{"given":"Ziwen","family":"He","sequence":"additional","affiliation":[{"name":"Marine Target Detection Research Group, Naval Aviation University, Yantai 264001, China"}]}],"member":"1968","published-online":{"date-parts":[[2022,2,24]]},"reference":[{"key":"ref_1","first-page":"803","article-title":"Progress and prospects of radar target detection and recognition technology for flying birds and unmanned aerial vehicles","volume":"9","author":"Chen","year":"2020","journal-title":"J. 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