{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,20]],"date-time":"2026-05-20T05:07:19Z","timestamp":1779253639457,"version":"3.51.4"},"reference-count":38,"publisher":"MDPI AG","issue":"18","license":[{"start":{"date-parts":[[2022,9,11]],"date-time":"2022-09-11T00:00:00Z","timestamp":1662854400000},"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>Under the condition of low signal-to-noise ratio, the target detection performance of radar decreases, which seriously affects the tracking and recognition for the long-range small targets. To solve it, this paper proposes a target detection algorithm using convolutional neural network to process graphically expressed range time series signals. First, the two-dimensional echo signal was processed graphically. Second, the graphical echo signal was detected by the improved convolutional neural network. The simulation results under the condition of low signal-to-noise ratio show that, compared with the multi-pulse accumulation detection method, the detection method based on convolutional neural network proposed in this paper has a higher target detection probability, which reflects the effectiveness of the method proposed in this paper.<\/jats:p>","DOI":"10.3390\/s22186868","type":"journal-article","created":{"date-parts":[[2022,9,13]],"date-time":"2022-09-13T04:05:41Z","timestamp":1663041941000},"page":"6868","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":12,"title":["Radar Target Detection Algorithm Using Convolutional Neural Network to Process Graphically Expressed Range Time Series Signals"],"prefix":"10.3390","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0177-3745","authenticated-orcid":false,"given":"Yan","family":"Dai","sequence":"first","affiliation":[{"name":"Beijing Institute of Radio Measurement, Beijing 100854, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Dan","family":"Liu","sequence":"additional","affiliation":[{"name":"Beijing Institute of Radio Measurement, Beijing 100854, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qingrong","family":"Hu","sequence":"additional","affiliation":[{"name":"Defense Technology Academy of China Aerospace Science and Industry Corporation Limited, Beijing 100070, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiaoli","family":"Yu","sequence":"additional","affiliation":[{"name":"Beijing Research Institute of Telemetry, Beijing 100094, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2022,9,11]]},"reference":[{"key":"ref_1","unstructured":"Richards, M.A. (2014). 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