{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,13]],"date-time":"2026-02-13T23:21:54Z","timestamp":1771024914112,"version":"3.50.1"},"reference-count":25,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2020,4,18]],"date-time":"2020-04-18T00:00:00Z","timestamp":1587168000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Foreign object debris (FOD) detection can be considered a kind of classification that distinguishes the measured signal as either containing FOD targets or only corresponding to ground clutter. In this paper, we propose a support vector domain description (SVDD) classifier with the particle swarm optimization (PSO) algorithm for FOD detection. The echo features of FOD and ground clutter received by the millimeter-wave radar are first extracted in the power spectrum domain as input eigenvectors of the classifier, followed with the parameters optimized by the PSO algorithm, and lastly, a PSO-SVDD classifier is established. However, since only ground clutter samples are utilized to train the SVDD classifier, overfitting inevitably occurs. Thus, a small number of samples with FOD are added in the training stage to further construct a PSO-NSVDD (NSVDD: SVDD with negative examples) classifier to achieve better classification performance. Experimental results based on measured data showed that the proposed methods could not only achieve a good detection performance but also significantly reduce the false alarm rate.<\/jats:p>","DOI":"10.3390\/s20082316","type":"journal-article","created":{"date-parts":[[2020,4,21]],"date-time":"2020-04-21T04:49:38Z","timestamp":1587444578000},"page":"2316","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":19,"title":["Small Foreign Object Debris Detection for Millimeter-Wave Radar Based on Power Spectrum Features"],"prefix":"10.3390","volume":"20","author":[{"given":"Peishuang","family":"Ni","sequence":"first","affiliation":[{"name":"Ministerial Key Laboratory of JGMT, Nanjing University of Science and Technology, Xiao Ling Wei200#, Nanjing 210094, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chen","family":"Miao","sequence":"additional","affiliation":[{"name":"Ministerial Key Laboratory of JGMT, Nanjing University of Science and Technology, Xiao Ling Wei200#, Nanjing 210094, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hui","family":"Tang","sequence":"additional","affiliation":[{"name":"Ministerial Key Laboratory of JGMT, Nanjing University of Science and Technology, Xiao Ling Wei200#, Nanjing 210094, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mengjie","family":"Jiang","sequence":"additional","affiliation":[{"name":"Ministerial Key Laboratory of JGMT, Nanjing University of Science and Technology, Xiao Ling Wei200#, Nanjing 210094, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wen","family":"Wu","sequence":"additional","affiliation":[{"name":"Ministerial Key Laboratory of JGMT, Nanjing University of Science and Technology, Xiao Ling Wei200#, Nanjing 210094, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2020,4,18]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Xu, H., Han, Z., Feng, S., Zhou, H., and Fang, Y. 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