{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,17]],"date-time":"2025-12-17T08:30:29Z","timestamp":1765960229637,"version":"build-2065373602"},"reference-count":30,"publisher":"MDPI AG","issue":"20","license":[{"start":{"date-parts":[[2022,10,13]],"date-time":"2022-10-13T00:00:00Z","timestamp":1665619200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Natural Science Foundation of China","award":["61973037","61871414"],"award-info":[{"award-number":["61973037","61871414"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>It is difficult for traditional signal-recognition methods to effectively classify and identify multiple emitter signals in a low SNR environment. This paper proposes a multi-emitter signal-feature-sorting and recognition method based on low-order cyclic statistics CWD time-frequency images and the YOLOv5 deep network model, which can quickly dissociate, label, and sort the multi-emitter signal features in the time-frequency domain under a low SNR environment. First, the denoised signal is extracted based on the low-order cyclic statistics of the typical modulation types of radiation source signals. Second, the time-frequency graph of multisource signals was obtained through CWD time-frequency analysis. The cyclic frequency was controlled to balance the noise suppression effect and operation time to achieve noise suppression of multisource signals at a low SNR. Finally, the YOLOv5s deep network model is used as a classifier to sort and identify the received signals from multiple radiation sources. The method proposed in this paper has high real-time performance. It can identify the radiation source signals of different modulation types with high accuracy under the condition of a low SNR.<\/jats:p>","DOI":"10.3390\/s22207783","type":"journal-article","created":{"date-parts":[[2022,10,14]],"date-time":"2022-10-14T01:44:13Z","timestamp":1665711853000},"page":"7783","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Low SNR Multi-Emitter Signal Sorting and Recognition Method Based on Low-Order Cyclic Statistics CWD Time-Frequency Images and the YOLOv5 Deep Learning Model"],"prefix":"10.3390","volume":"22","author":[{"given":"Dingkun","family":"Huang","sequence":"first","affiliation":[{"name":"Technology on Electromechanical Dynamic Control Laboratory, School of Mechatronical Engineering, Beijing Institute of Technology, Beijing 100081, China"}]},{"given":"Xiaopeng","family":"Yan","sequence":"additional","affiliation":[{"name":"Technology on Electromechanical Dynamic Control Laboratory, School of Mechatronical Engineering, Beijing Institute of Technology, Beijing 100081, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6448-4839","authenticated-orcid":false,"given":"Xinhong","family":"Hao","sequence":"additional","affiliation":[{"name":"Technology on Electromechanical Dynamic Control Laboratory, School of Mechatronical Engineering, Beijing Institute of Technology, Beijing 100081, China"}]},{"given":"Jian","family":"Dai","sequence":"additional","affiliation":[{"name":"Technology on Electromechanical Dynamic Control Laboratory, School of Mechatronical Engineering, Beijing Institute of Technology, Beijing 100081, China"}]},{"given":"Xinwei","family":"Wang","sequence":"additional","affiliation":[{"name":"Technology on Electromechanical Dynamic Control Laboratory, School of Mechatronical Engineering, Beijing Institute of Technology, Beijing 100081, China"}]}],"member":"1968","published-online":{"date-parts":[[2022,10,13]]},"reference":[{"key":"ref_1","first-page":"94","article-title":"A new blind detection algorithm based on cyclic statistics","volume":"6","author":"Yu","year":"2017","journal-title":"Tactical Missile Technol."},{"key":"ref_2","first-page":"76","article-title":"Research on VHF Band Signal Modulation Classification and Recognition Methods Based on Algorithm of First-Order Cyclic Moment","volume":"30","author":"Yang","year":"2014","journal-title":"Telecom Sci."},{"key":"ref_3","first-page":"617","article-title":"Modulation recognition method based on convolutional neural network and cyclic spectrum images","volume":"19","author":"Lin","year":"2021","journal-title":"J. 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