{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,21]],"date-time":"2026-07-21T14:20:43Z","timestamp":1784643643106,"version":"3.55.0"},"reference-count":38,"publisher":"MDPI AG","issue":"16","license":[{"start":{"date-parts":[[2024,8,15]],"date-time":"2024-08-15T00:00:00Z","timestamp":1723680000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Key Research Projects of Tianjin Municipal Education Commission in China","award":["2022ZD005"],"award-info":[{"award-number":["2022ZD005"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Non-cooperative targets, such as birds and unmanned aerial vehicles (UAVs), are typical low-altitude, slow, and small (LSS) targets with low observability. Radar observations in such scenarios are often complicated by strong motion clutter originating from sources like airplanes and cars. Hence, distinguishing between birds and UAVs in environments with strong motion clutter is crucial for improving target monitoring performance and ensuring flight safety. To address the impact of strong motion clutter on discriminating between UAVs and birds, we propose a frequency correlation dual-SVD (singular value decomposition) reconstruction method. This method exploits the strong power and spectral correlation characteristics of motion clutter, contrasted with the weak scattering characteristics of bird and UAV targets, to effectively suppress clutter. Unlike traditional clutter suppression methods based on SVD, our method avoids residual clutter or target loss while preserving the micro-motion characteristics of the targets. Based on the distinct micro-motion characteristics of birds and UAVs, we extract two key features: the sum of normalized large eigenvalues of the target\u2019s micro-motion component and the energy entropy of the time\u2013frequency spectrum of the radar echoes. Subsequently, the kernel fuzzy c-means algorithm is applied to classify bird and UAV targets. The effectiveness of our proposed method is validated through results using both simulation and experimental data.<\/jats:p>","DOI":"10.3390\/s24165298","type":"journal-article","created":{"date-parts":[[2024,8,16]],"date-time":"2024-08-16T04:29:57Z","timestamp":1723782597000},"page":"5298","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Motion Clutter Suppression for Non-Cooperative Target Identification Based on Frequency Correlation Dual-SVD Reconstruction"],"prefix":"10.3390","volume":"24","author":[{"given":"Weikun","family":"He","sequence":"first","affiliation":[{"name":"Tianjin Key Lab for Advanced Signal Processing, Civil Aviation University of China, Tianjin 300300, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yichuan","family":"Luo","sequence":"additional","affiliation":[{"name":"Tianjin Key Lab for Advanced Signal Processing, Civil Aviation University of China, Tianjin 300300, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaoxiao","family":"Shang","sequence":"additional","affiliation":[{"name":"Tianjin Key Lab for Advanced Signal Processing, Civil Aviation University of China, Tianjin 300300, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2024,8,15]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Alhaji Musa, S., Raja Abdullah, R.S.A., Sali, A., Ismail, A., and Abdul Rashid, N.E. 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