{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,17]],"date-time":"2026-01-17T07:45:41Z","timestamp":1768635941396,"version":"3.49.0"},"reference-count":38,"publisher":"MDPI AG","issue":"24","license":[{"start":{"date-parts":[[2024,12,11]],"date-time":"2024-12-11T00:00:00Z","timestamp":1733875200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Aiming at the problems, in which the traditional radar signal sorting method has high requirements for manual experience and poor adaptability, and considering the differences in received power caused by radar beam scanning under long-term observation, an end-to-end signal sorting method based on the instance segmentation network SOLOv2 and using an antenna scan pattern (ASP) is proposed in this letter. Firstly, the interleaved pulse sequences of multiple radar signals with various inter-pulse modulation types, scan patterns, and gain patterns are simulated, mimetic image mapping is constructed to visualize the interleaved pulse sequences as mimetic point graphs, and the index relationship between pulses and pixel points is recorded. Subsequently, the SOLOv2 instance segmentation network is used to segment the mimetic point graph at the pixel level, thereby clustering the discrete pixel points in the image. Finally, based on the index relationship recorded during the construction of the mimetic image mapping, the clustering results of points in the image are traced back to the clustering of pulses, achieving end-to-end intelligent radar signal sorting. Through simulation experiments, it was verified that, compared with YOLOv8-based, U-Net-based, and traditional signal sorting methods, the sorting accuracy of the proposed method increased by 9.26%, 11.17%, and 24.55% in the scenario of five signals with 30% missing pulse ratio (MPR), and increased by 13.33%, 18.88%, and 23.94% in the scenario of five signals with 30% spurious pulse ratio (SPR), respectively. The results show that by introducing the stable parameter, namely ASP, the proposed method can achieve signal sorting with highly overlapping parameters and adapt to non-ideal conditions with measurement errors, missing pulses, and spurious pulses.<\/jats:p>","DOI":"10.3390\/rs16244639","type":"journal-article","created":{"date-parts":[[2024,12,11]],"date-time":"2024-12-11T06:44:05Z","timestamp":1733899445000},"page":"4639","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Radar Signal Sorting Method with Mimetic Image Mapping Based on Antenna Scan Pattern via SOLOv2 Network"],"prefix":"10.3390","volume":"16","author":[{"given":"Tao","family":"Chen","sequence":"first","affiliation":[{"name":"College of Information and Communication Engineering, Harbin Engineering University, Harbin 150001, China"},{"name":"Key Laboratory of Advanced Marine Communication and Information Technology, Ministry of Industry and Information Technology, Harbin 150001, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0002-2439-1715","authenticated-orcid":false,"given":"Xiaoqi","family":"Guo","sequence":"additional","affiliation":[{"name":"College of Information and Communication Engineering, Harbin Engineering University, Harbin 150001, China"},{"name":"Key Laboratory of Advanced Marine Communication and Information Technology, Ministry of Industry and Information Technology, Harbin 150001, China"}]},{"given":"Jinxin","family":"Li","sequence":"additional","affiliation":[{"name":"College of Information and Communication Engineering, Harbin Engineering University, Harbin 150001, China"},{"name":"Key Laboratory of Advanced Marine Communication and Information Technology, Ministry of Industry and Information Technology, Harbin 150001, China"}]}],"member":"1968","published-online":{"date-parts":[[2024,12,11]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"226","DOI":"10.1049\/ip-f-1.1985.0054","article-title":"Signal sorting in ESM systems","volume":"132","author":"Whittall","year":"1985","journal-title":"IEE Proc. Part F Commun. Radar Signal Process."},{"key":"ref_2","unstructured":"Haigh, K., and Andrusenko, J. (2021). Cognitive Electronic Warfare: An Artificial Intelligence Approach, Artech House."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"5899","DOI":"10.1109\/TGRS.2017.2716935","article-title":"Command and control for multifunction phased array radar","volume":"55","author":"Weber","year":"2017","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"149","DOI":"10.1049\/ip-f-2.1989.0025","article-title":"New techniques for the deinterleaving of repetitive sequences","volume":"136","author":"Mardia","year":"1989","journal-title":"IEE Proc. Part F Commun. Radar Signal Process."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"98","DOI":"10.1049\/ip-f-2.1992.0012","article-title":"Improved algorithm for the deinterleaving of radar pulses","volume":"139","year":"1992","journal-title":"IEE Proc. Part F Commun. Radar Signal Process."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"885","DOI":"10.1109\/TSP.2018.2886149","article-title":"Deinterleaving of mixtures of renewal processes","volume":"67","author":"Young","year":"2018","journal-title":"IEEE Trans. Signal Process."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"1624","DOI":"10.1109\/TAES.2018.2874139","article-title":"Classification, denoising, and deinterleaving of pulse streams with recurrent neural networks","volume":"55","author":"Liu","year":"2018","journal-title":"IEEE Trans. Aerosp. Electron. Syst."},{"key":"ref_8","first-page":"989","article-title":"Deep ToA mask-based recursive radar pulse deinterleaving","volume":"59","author":"Xiang","year":"2022","journal-title":"IEEE Trans. Aerosp. Electron. Syst."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"613","DOI":"10.1109\/LSP.2023.3254439","article-title":"Separation of Interleaved Pulse Stream Based on Directed Acyclic Graphs","volume":"30","author":"Xie","year":"2023","journal-title":"IEEE Signal Process. Lett."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"104162","DOI":"10.1016\/j.dsp.2023.104162","article-title":"A radar pulse train deinterleaving method for missing and short observations","volume":"141","author":"Guo","year":"2023","journal-title":"Digit. Signal Process."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Su, S., Fu, X., Zhao, C., Yang, J., Xie, M., and Gao, Z. (2019, January 11\u201313). Unsupervised k-means combined with SOFM structure adaptive radar signal sorting algorithm. Proceedings of the 2019 IEEE International Conference on Signal, Information and Data Processing (ICSIDP), Chongqing, China.","DOI":"10.1109\/ICSIDP47821.2019.9172926"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"91","DOI":"10.1109\/LSP.2020.3044259","article-title":"Multi-function radar signal sorting based on complex network","volume":"28","author":"Chi","year":"2020","journal-title":"IEEE Signal Process. Lett."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"424","DOI":"10.1109\/LSP.2021.3139528","article-title":"Subspace decomposition based adaptive density peak clustering for radar signals sorting","volume":"29","author":"Lang","year":"2021","journal-title":"IEEE Signal Process. Lett."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"26","DOI":"10.1109\/MAES.2023.3268020","article-title":"Incremental deinterleaving of radar emitters","volume":"38","author":"Scholl","year":"2023","journal-title":"IEEE Aerosp. Electron. Syst. Mag."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Dai, D., Qiao, G., Zhang, C., Tian, R., and Zhang, S. (2023). A Sorting Method of SAR Emitter Signal Sorting Based on Self-Supervised Clustering. Remote Sens., 15.","DOI":"10.3390\/rs15071867"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"104434","DOI":"10.1016\/j.dsp.2024.104434","article-title":"DOA estimation and signal sorting methods of multi-baseline polarized interferometer","volume":"148","author":"Qu","year":"2024","journal-title":"Digit. Signal Process."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"13","DOI":"10.1016\/j.dsp.2019.06.009","article-title":"Simultaneous direct position determination and pulse deinterleaving by a moving receiver","volume":"93","author":"Sabeti","year":"2019","journal-title":"Digit. Signal Process."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"4767","DOI":"10.1109\/TAES.2020.3004208","article-title":"Deinterleaving of pulse streams with denoising autoencoders","volume":"56","author":"Li","year":"2020","journal-title":"IEEE Trans. Aerosp. Electron. Syst."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Gasperini, S., Paschali, M., Hopke, C., Wittmann, D., and Navab, N. (2020, January 4\u20138). Signal clustering with class-independent segmentation. Proceedings of the ICASSP 2020\u20142020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Barcelona, Spain.","DOI":"10.1109\/ICASSP40776.2020.9053409"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"5806","DOI":"10.1109\/TSP.2022.3229630","article-title":"A radar signal deinterleaving method based on semantic segmentation with neural network","volume":"70","author":"Chao","year":"2022","journal-title":"IEEE Trans. Signal Process."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"90","DOI":"10.1016\/j.inffus.2021.09.007","article-title":"UAV swarm based radar signal sorting via multi-source data fusion: A deep transfer learning framework","volume":"78","author":"Wan","year":"2022","journal-title":"Inf. Fusion"},{"key":"ref_22","first-page":"1351","article-title":"End-to-end radar signal sorting based on deep segmentation","volume":"45","author":"Fuyue","year":"2023","journal-title":"Syst. Eng. Electron."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"1162","DOI":"10.1109\/LSP.2023.3309161","article-title":"A learning-based signal parameter extraction approach for multi-source frequency-hopping signal sorting","volume":"30","author":"Wang","year":"2023","journal-title":"IEEE Signal Process. Lett."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"2000","DOI":"10.1109\/LCOMM.2020.2995842","article-title":"Radar emitter classification with attention-based multi-RNNs","volume":"24","author":"Li","year":"2020","journal-title":"IEEE Commun. Lett."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"753","DOI":"10.1109\/LSP.2023.3287404","article-title":"A Novel Radar Signals Sorting Method via Residual Graph Convolutional Network","volume":"30","author":"Lang","year":"2023","journal-title":"IEEE Signal Process. Lett."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Liu, H., Wang, L., and Wang, G. (2024). Emitter Signal Deinterleaving Based on Single PDW with Modulation-Hypothesis-Augmented Transformer. Remote Sens., 16.","DOI":"10.3390\/rs16203830"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Zhang, X., Hao, C., Zhang, S., and Zheng, L. (2017, January 23\u201328). Characterization and identification of active electronically scanned array radar. Proceedings of the 2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Fort Worth, TX, USA.","DOI":"10.1109\/IGARSS.2017.8127454"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"1350","DOI":"10.1049\/iet-rsn.2018.5525","article-title":"Discrete wavelet transform based unsupervised underdetermined blind source separation methodology for radar pulse deinterleaving using antenna scan pattern","volume":"13","author":"Dutt","year":"2019","journal-title":"IET Radar Sonar Navig."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Wang, C., Wang, Y., Li, X., and Ke, D. (2022, January 15\u201317). A deinterleaving method for mechanical-scanning radar signals based on deep learning. Proceedings of the 2022 7th International Conference on Intelligent Computing and Signal Processing (ICSP), Xi\u2019an, China.","DOI":"10.1109\/ICSP54964.2022.9778808"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"7789","DOI":"10.1007\/s11760-024-03428-2","article-title":"Object detection based deinterleaving of radar signals using deep learning for cognitive EW","volume":"18","year":"2024","journal-title":"Signal Image Video Process."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"He, K., Gkioxari, G., Doll\u00e1r, P., and Girshick, R. (2017, January 22\u201329). Mask r-cnn. Proceedings of the IEEE International Conference on Computer Vision, Venice, Italy.","DOI":"10.1109\/ICCV.2017.322"},{"key":"ref_32","first-page":"17721","article-title":"Solov2: Dynamic and fast instance segmentation","volume":"33","author":"Wang","year":"2020","journal-title":"Adv. Neural Inf. Process. Syst"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Wang, X., Kong, T., Shen, C., Jiang, Y., and Li, L. (2020). Solo: Segmenting objects by locations. Computer Vision\u2014ECCV 2020, Proceedings of the 16th European Conference, Glasgow, UK, 23\u201328 August 2020, Springer. Proceedings, Part XVIII 16.","DOI":"10.1007\/978-3-030-58523-5_38"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Fang, Y., Yang, S., Wang, X., Li, Y., Fang, C., Shan, Y., Feng, B., and Liu, W. (2021, January 11\u201317). Instances as queries. Proceedings of the IEEE\/CVF international Conference on Computer Vision, Montreal, BC, Canada.","DOI":"10.1109\/ICCV48922.2021.00683"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"908","DOI":"10.1109\/TCSVT.2021.3069094","article-title":"Learning clustering for motion segmentation","volume":"32","author":"Xu","year":"2021","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"393","DOI":"10.1093\/bib\/bbz170","article-title":"Deep learning-based clustering approaches for bioinformatics","volume":"22","author":"Karim","year":"2021","journal-title":"Brief. Bioinform."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"1022","DOI":"10.1109\/LSP.2023.3284893","article-title":"Radar Pulse Stream Clustering Based on MaskRCNN Instance Segmentation Network","volume":"30","author":"Chen","year":"2023","journal-title":"IEEE Signal Process. Lett."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"466","DOI":"10.1049\/iet-rsn.2017.0354","article-title":"Automatic antenna scan type classification for next-generation electronic warfare receivers","volume":"12","author":"Ayazgok","year":"2018","journal-title":"IET Radar Sonar Navig."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/16\/24\/4639\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T16:52:20Z","timestamp":1760115140000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/16\/24\/4639"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,12,11]]},"references-count":38,"journal-issue":{"issue":"24","published-online":{"date-parts":[[2024,12]]}},"alternative-id":["rs16244639"],"URL":"https:\/\/doi.org\/10.3390\/rs16244639","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,12,11]]}}}