{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,4]],"date-time":"2026-08-04T06:55:03Z","timestamp":1785826503193,"version":"3.56.0"},"reference-count":31,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2021,3,22]],"date-time":"2021-03-22T00:00:00Z","timestamp":1616371200000},"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":["61971368"],"award-info":[{"award-number":["61971368"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["U20A20162"],"award-info":[{"award-number":["U20A20162"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003392","name":"Natural Science Foundation of Fujian Province","doi-asserted-by":"publisher","award":["2019J01003"],"award-info":[{"award-number":["2019J01003"]}],"id":[{"id":"10.13039\/501100003392","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>A micro-Doppler signature (m-DS) based on the rotation of drone blades is an effective way to detect and identify small drones. Deep-learning-based recognition algorithms can achieve higher recognition performance, but they needs a large amount of sample data to train models. In addition to the hovering state, the signal samples of small unmanned aerial vehicles (UAVs) should also include flight dynamics, such as vertical, pitch, forward and backward, roll, lateral, and yaw. However, it is difficult to collect all dynamic UAV signal samples under actual flight conditions, and these dynamic flight characteristics will lead to the deviation of the original features, thus affecting the performance of the recognizer. In this paper, we propose a small UAV m-DS recognition algorithm based on dynamic feature enhancement. We extract the combined principal component analysis and discrete wavelet transform (PCA-DWT) time\u2013frequency characteristics and texture features of the UAV\u2019s micro-Doppler signal and use a dynamic attribute-guided augmentation (DAGA) algorithm to expand the feature domain for model training to achieve an adaptive, accurate, and efficient multiclass recognition model in complex environments. After the training model is stable, the average recognition accuracy rate can reach 98% during dynamic flight.<\/jats:p>","DOI":"10.3390\/rs13061205","type":"journal-article","created":{"date-parts":[[2021,3,22]],"date-time":"2021-03-22T12:55:07Z","timestamp":1616417707000},"page":"1205","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":24,"title":["UAV Recognition Based on Micro-Doppler Dynamic Attribute-Guided Augmentation Algorithm"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9682-4159","authenticated-orcid":false,"given":"Caidan","family":"Zhao","sequence":"first","affiliation":[{"name":"Department of Informatics, Xiamen University, Xiamen 361001, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0963-1453","authenticated-orcid":false,"given":"Gege","family":"Luo","sequence":"additional","affiliation":[{"name":"Department of Electronic Science and Engineering, Xiamen University, Xiamen 361001, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0068-7298","authenticated-orcid":false,"given":"Yilin","family":"Wang","sequence":"additional","affiliation":[{"name":"Department of Informatics, Xiamen University, Xiamen 361001, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Caiyun","family":"Chen","sequence":"additional","affiliation":[{"name":"Department of Informatics, Xiamen University, Xiamen 361001, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhiqiang","family":"Wu","sequence":"additional","affiliation":[{"name":"College of Engineering, Tibet University, Lhasa 850000, China"},{"name":"Department of Electrical Engineering, Wright State University, Dayton, OH 45435, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2021,3,22]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"2048","DOI":"10.1109\/TIM.2018.2811256","article-title":"Cyclostationary Phase Analysis on Micro-Doppler Parameters for Radar-Based Small UAVs Detection","volume":"67","author":"Zhao","year":"2018","journal-title":"IEEE Trans. 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