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By CEEMD method, sea clutter signal which contains small target can be decomposed into a series of intrinsic mode function (IMF) components, pick out high\u2010frequency components which contain more noise by autocorrelation function, and perform wavelet transform on them. The de\u2010noised components and remaining components are used to reconstruct clear signal. In view of the chaotic characteristics of sea clutter, we use Volterra filter to establish adaptive prediction model, detect low\u2010flying small target hiding in sea clutter background from the prediction error, and compare the root mean square error (RMSE) before and after de\u2010noising to evaluate de\u2010noising effect. Experimental results show that the joint algorithm can effectively remove noise and reduce the RMSE by 40% at least. Volterra prediction model can directly detect low\u2010flying small target under sea clutter background from the prediction error in the cases of high signal\u2010to\u2010noise ratio (SNR). In the cases of low SNR, after de\u2010noised by joint algorithm, Volterra prediction model can also detect the low\u2010flying small target clearly.<\/jats:p>","DOI":"10.1155\/2018\/1513591","type":"journal-article","created":{"date-parts":[[2018,7,4]],"date-time":"2018-07-04T23:31:09Z","timestamp":1530747069000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["Detection of Low\u2010Flying Target under the Sea Clutter Background Based on Volterra Filter"],"prefix":"10.1155","volume":"2018","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3204-3457","authenticated-orcid":false,"given":"Hongyan","family":"Xing","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2439-5276","authenticated-orcid":false,"given":"Yan","family":"Yan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","published-online":{"date-parts":[[2018,7,4]]},"reference":[{"key":"e_1_2_9_1_2","doi-asserted-by":"publisher","DOI":"10.3390\/s17051177"},{"key":"e_1_2_9_2_2","doi-asserted-by":"publisher","DOI":"10.1109\/MTS.2014.2319951"},{"key":"e_1_2_9_3_2","doi-asserted-by":"crossref","unstructured":"GorajZ. 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