{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T01:41:17Z","timestamp":1760233277675,"version":"build-2065373602"},"reference-count":33,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2022,12,26]],"date-time":"2022-12-26T00:00:00Z","timestamp":1672012800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61871092","U20B2070"],"award-info":[{"award-number":["61871092","U20B2070"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Electromagnetic data annotation is one of the most important steps in many signal processing applications, e.g., radar signal deinterleaving and radar mode analysis. This work considers cooperative electromagnetic data annotation from multiple reconnaissance receivers\/platforms. By exploiting the inherent correlation of the electromagnetic signal, as well as the correlation of the observations from multiple receivers, a low-rank matrix recovery formulation is proposed for the cooperative annotation problem. Specifically, considering the measured parameters of the same emitter should be roughly the same at different platforms, the cooperative annotation is modeled as a low-rank matrix recovery problem, which is solved iteratively either by the rank minimization method or the maximum-rank decomposition method. A comparison of the two methods, with the traditional annotation method on both the synthetic and real data, is given. Numerical experiments show that the proposed methods can effectively recover missing annotations and correct annotation errors.<\/jats:p>","DOI":"10.3390\/rs15010121","type":"journal-article","created":{"date-parts":[[2022,12,27]],"date-time":"2022-12-27T02:53:11Z","timestamp":1672109591000},"page":"121","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Cooperative Electromagnetic Data Annotation via Low-Rank Matrix Completion"],"prefix":"10.3390","volume":"15","author":[{"given":"Wei","family":"Zhang","sequence":"first","affiliation":[{"name":"School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China"},{"name":"Science and Technology on Electronic Information Control Laboratory, Chengdu 610036, China"}]},{"given":"Jian","family":"Yang","sequence":"additional","affiliation":[{"name":"Northern Institute of Electronic Equipment of China, Beijing 100089, China"}]},{"given":"Qiang","family":"Li","sequence":"additional","affiliation":[{"name":"School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China"}]},{"given":"Jingran","family":"Lin","sequence":"additional","affiliation":[{"name":"School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China"}]},{"given":"Huaizong","family":"Shao","sequence":"additional","affiliation":[{"name":"School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China"}]},{"given":"Guomin","family":"Sun","sequence":"additional","affiliation":[{"name":"School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China"}]}],"member":"1968","published-online":{"date-parts":[[2022,12,26]]},"reference":[{"key":"ref_1","first-page":"418","article-title":"Progress in Radar Emitter Signal Deinterleaving","volume":"11","author":"Sui","year":"2022","journal-title":"J. Radars"},{"key":"ref_2","unstructured":"Fu, Y., and Wang, X. (2017, January 25\u201326). Radar signal recognition based on modified semi-supervised SVM algorithm. Proceedings of the 2017 IEEE 2nd Advanced Information Technology, Electronic and Automation Control Conference (IAEAC), Chongqing, China."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1733","DOI":"10.1109\/TAES.2021.3122411","article-title":"Model based Representation and Deinterleaving of Mixed Radar Pulse Sequences with Neural Ma-chineTranslation Network","volume":"58","author":"Zhu","year":"2022","journal-title":"IEEE Trans. Aerosp. Electron. Syst."},{"key":"ref_4","first-page":"1300","article-title":"Panorama of national defense big data","volume":"38","author":"He","year":"2016","journal-title":"Syst. Eng. Electron."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"73","DOI":"10.1016\/j.neucom.2015.11.110","article-title":"A text feature-based approach for literature mining of IncRNA-protein interactions","volume":"206","author":"Li","year":"2016","journal-title":"Neurocomputing"},{"key":"ref_6","unstructured":"Arlotta, L., Crescenz, V., Mecca, G., and Merialdo, P. (2003, January 12\u201313). Automatic annotation of data extracted from large web sites. Proceedings of the 6th International Workshop on Web and Databases, San Diego, CA, USA."},{"key":"ref_7","first-page":"1733","article-title":"Deep web data annotation method based on result schema","volume":"31","author":"Li","year":"2011","journal-title":"J. Comput. Appl."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/1970392.1970395","article-title":"Robust principal component analysis?","volume":"58","author":"Candes","year":"2011","journal-title":"J. ACM (JACM)"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"3047","DOI":"10.1109\/TIT.2011.2173156","article-title":"Robust PCA via Outlier Pursuit","volume":"58","author":"Xu","year":"2012","journal-title":"IEEE Trans. Inf. Theory"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"2053","DOI":"10.1109\/TIT.2010.2044061","article-title":"The power of convex relaxation: Near optimal matrix completion","volume":"56","author":"Candes","year":"2010","journal-title":"IEEE Trans. Inf. Theory"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"18484","DOI":"10.1109\/ACCESS.2018.2818794","article-title":"End to end learning from spectrum data: A deep learning approach for wireless signal identification in spectrum monitoring applications","volume":"6","author":"Kulin","year":"2018","journal-title":"IEEE Access"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"471","DOI":"10.1137\/070697835","article-title":"Guaranteed minimum rank solutions of linear matrix equations via nuclear norm minimiza-tion","volume":"52","author":"Recht","year":"2010","journal-title":"SIAM Rev."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"333","DOI":"10.1007\/s12532-012-0044-1","article-title":"Solving a low rank factorization model for matrix completion by a nonlinear successive over relaxation algorithm","volume":"4","author":"Wen","year":"2012","journal-title":"Math. Program. Comput."},{"key":"ref_14","first-page":"1089","article-title":"SpaRCS: Recovering low-rank and sparse matrices from compressive meas-urements","volume":"24","author":"Waters","year":"2011","journal-title":"Neural Inf. Process. Syst."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"3083","DOI":"10.1109\/TBME.2013.2266096","article-title":"High-Resolution Cardiovascular MRI by Inte-grating Parallel Imaging With Low-Rank and Sparse Modeling","volume":"60","author":"Christodoulou","year":"2013","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Sykulski, M. (2015, July 31). RobustPCA: Decompose a Matrix into Low-Rank and Sparse Components. Available online: https:\/\/CRAN.R-project.org\/package=rpca.","DOI":"10.32614\/CRAN.package.rpca"},{"key":"ref_17","unstructured":"Chen, Y., Xu, H., Caramanis, C., and Sanghavi, S. (July, January 28). Robust matrix completion and corrupted columns. Proceedings of the 28th International Conference on Machine Learning, Bellevue, WA, USA."},{"key":"ref_18","first-page":"1665","article-title":"Restricted strong convexity and weighted matrix completion: Optimal bounds with noise","volume":"5","author":"Negahban","year":"2012","journal-title":"J. Mach. Learn. Res."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"237","DOI":"10.1109\/TIT.2011.2171521","article-title":"A geometric approach to low-rank matrix completion","volume":"58","author":"Dai","year":"2012","journal-title":"IEEE Trans. Inf. Theory"},{"key":"ref_20","first-page":"1703","article-title":"Design of weighted matrix completion model in image inpainting","volume":"38","author":"Bai","year":"2016","journal-title":"Syst. Eng. Electron."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"4182","DOI":"10.1109\/TIP.2017.2703120","article-title":"Label information guided graph construction for semi-supervised learning","volume":"26","author":"Zhang","year":"2017","journal-title":"IEEE Trans. Image Process."},{"key":"ref_22","first-page":"2057","article-title":"Matrix completion from noisy entries","volume":"11","author":"Keshavan","year":"2010","journal-title":"J. Mach. Learn. Res."},{"key":"ref_23","first-page":"3413","article-title":"A simpler approach to matrix completion","volume":"12","author":"Recht","year":"2011","journal-title":"J. Mach. Learn. Res."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"925","DOI":"10.1109\/JPROC.2009.2035722","article-title":"Matrix completion with noise","volume":"98","author":"Candes","year":"2010","journal-title":"Proc. IEEE"},{"key":"ref_25","first-page":"1504","article-title":"SAR image denoising via fast weighted nuclear norm minimization","volume":"41","author":"Wang","year":"2019","journal-title":"Syst. Eng. Electron."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"303","DOI":"10.1007\/BF00927673","article-title":"Multiplier and gradient methods","volume":"4","author":"Hestenes","year":"1969","journal-title":"J. Optim. Theory Appl."},{"key":"ref_27","first-page":"726","article-title":"Distributed method for joint power allocation and admission control based on ADMM Framework","volume":"45","author":"Lin","year":"2016","journal-title":"J. Univ. Electron. Sci. Technol. China"},{"key":"ref_28","first-page":"615","article-title":"An accelerated proximal gradient algorithm for nuclear norm regularized linear least squares problems","volume":"6","author":"Toh","year":"2010","journal-title":"Pac. J. Optim."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"61","DOI":"10.1109\/MGRS.2015.2441912","article-title":"Missing information reconstruction of remote sensing data: A technical review","volume":"3","author":"Shen","year":"2015","journal-title":"IEEE Geosci. Remote Sens. Mag."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"163","DOI":"10.1109\/TGRS.2012.2237408","article-title":"Patch-based information reconstruction of cloud-contaminated multitemporal images","volume":"52","author":"Lin","year":"2014","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Parikh, N., and Boyd, S. (2014). Proximal Algorithms, Foundations and Trends in Optimization, Now Publishers Inc.","DOI":"10.1561\/2400000003"},{"key":"ref_32","first-page":"195","article-title":"Semi-supervised k-nearest neighbor classification method","volume":"18","author":"Chen","year":"2013","journal-title":"J. Image Graph."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"4108","DOI":"10.1109\/TSP.2020.3001906","article-title":"Penalty dual Decompositon method for nonsmooth nonconvex optimization Part I:Algorithm and Con-vergence Analysis","volume":"68","author":"Shi","year":"2020","journal-title":"IEEE Trans. Signal Process."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/1\/121\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T01:51:24Z","timestamp":1760147484000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/1\/121"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,12,26]]},"references-count":33,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2023,1]]}},"alternative-id":["rs15010121"],"URL":"https:\/\/doi.org\/10.3390\/rs15010121","relation":{},"ISSN":["2072-4292"],"issn-type":[{"type":"electronic","value":"2072-4292"}],"subject":[],"published":{"date-parts":[[2022,12,26]]}}}