{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,27]],"date-time":"2026-06-27T15:53:15Z","timestamp":1782575595839,"version":"3.54.5"},"reference-count":23,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2024,4,2]],"date-time":"2024-04-02T00:00:00Z","timestamp":1712016000000},"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":["62271031"],"award-info":[{"award-number":["62271031"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"National Key Lab Foundation of Microwave Imaging Technology","award":["62271031"],"award-info":[{"award-number":["62271031"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Sea clutter usually greatly affects the target detection and identification performance of marine surveillance radars. In order to reduce the impact of sea clutter, a novel sea clutter suppression method based on chaos prediction is proposed in this paper. The method combines a generator trained by Generative Adversarial Networks (GAN) with a Long Short-Term Memory (LSTM) network to accomplish sea clutter prediction. By exploiting the generator\u2019s ability to learn the distribution of unlabeled data, the accuracy of sea clutter prediction is improved compared with the classical LSTM-based model. Furthermore, effective suppression of sea clutter and improvements in the signal-to-clutter ratio of echo were achieved through clutter cancellation. Experimental results on real data demonstrated the effectiveness of the proposed method.<\/jats:p>","DOI":"10.3390\/rs16071260","type":"journal-article","created":{"date-parts":[[2024,4,3]],"date-time":"2024-04-03T00:58:38Z","timestamp":1712105918000},"page":"1260","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Sea Clutter Suppression Based on Chaotic Prediction Model by Combining the Generator and Long Short-Term Memory Networks"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8273-3387","authenticated-orcid":false,"given":"Jindong","family":"Yu","sequence":"first","affiliation":[{"name":"School of Electronic and Information Engineering, Beihang University, Beijing 100191, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Baojing","family":"Pan","sequence":"additional","affiliation":[{"name":"School of Electronic and Information Engineering, Beihang University, Beijing 100191, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ze","family":"Yu","sequence":"additional","affiliation":[{"name":"School of Electronic and Information Engineering, Beihang University, Beijing 100191, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hongling","family":"Zhu","sequence":"additional","affiliation":[{"name":"Beijing Institute of Radio Metrology and Measurement, Beijing 100854, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hanfu","family":"Li","sequence":"additional","affiliation":[{"name":"Harbin Institute of Technology, Harbin 150001, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chao","family":"Li","sequence":"additional","affiliation":[{"name":"Harbin Institute of Technology, Harbin 150001, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6876-6051","authenticated-orcid":false,"given":"Hezhi","family":"Sun","sequence":"additional","affiliation":[{"name":"Institute of Remote Sensing Satellite, China Academy of Space Technology, Beijing 100094, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2024,4,2]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"95","DOI":"10.1109\/5.362751","article-title":"Detection of signals in chaos","volume":"83","author":"Haykin","year":"1995","journal-title":"Proc. IEEE"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"777","DOI":"10.1063\/1.166275","article-title":"Chaotic dynamics of sea clutter","volume":"7","author":"Haykin","year":"1997","journal-title":"Chaos Interdiscip. J. Nonlinear Sci."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1491","DOI":"10.1109\/TGRS.2003.811690","article-title":"A multiple-model prediction approach for sea clutter modeling","volume":"41","author":"Xie","year":"2003","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Wang, Q., and Wen, B. (2009, January 11\u201313). Active Learning Artificial Neural Networks Ensemble for HF Ground Wave Radar Sea Clutter Predicting. Proceedings of the 2009 International Conference on Computational Intelligence and Software Engineering, Wuhan, China.","DOI":"10.1109\/CISE.2009.5366681"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Gao, Z., and Chen, L. (2015, January 12\u201313). Sea Clutter Sequences Regression Prediction Based on PSO-GRNN Method. Proceedings of the 2015 8th International Symposium on Computational Intelligence and Design (ISCID), Hangzhou, China.","DOI":"10.1109\/ISCID.2015.249"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"3409","DOI":"10.1109\/TSP.2004.837418","article-title":"Prediction of chaotic time series based on the recurrent predictor neural network","volume":"52","author":"Han","year":"2004","journal-title":"IEEE Trans. Signal Process."},{"key":"ref_7","unstructured":"Salehinejad, H., Sankar, S., Barfett, J., Colak, E., and Valaee, S. (2017). Recent advances in recurrent neural networks. arXiv."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"1735","DOI":"10.1162\/neco.1997.9.8.1735","article-title":"Long short-term memory","volume":"9","author":"Hochreiter","year":"1997","journal-title":"Neural Comput."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Ma, L., Wu, J., Zhang, J., Wu, Z., Jeon, G., Tan, M., and Zhang, Y. (2019). Sea Clutter Amplitude Prediction Using a Long Short-Term Memory Neural Network. Remote Sens., 11.","DOI":"10.3390\/rs11232826"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"1235","DOI":"10.1162\/neco_a_01199","article-title":"A review of recurrent neural networks: LSTM cells and network architectures","volume":"31","author":"Yu","year":"2019","journal-title":"Neural Comput."},{"key":"ref_11","first-page":"2672","article-title":"Generative Adversarial Nets","volume":"27","author":"Goodfellow","year":"2014","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_12","unstructured":"Li, C.L., Chang, W.C., Cheng, Y., Yang, Y., and P\u00f3czos, B. (2017). Mmd gan: Towards deeper understanding of moment matching network. Adv. Neural Inf. Process. Syst., 30."},{"key":"ref_13","unstructured":"Durgadevi, M. (2021, January 8\u201310). Generative adversarial network (gan): A general review on different variants of gan and applications. Proceedings of the 2021 6th International Conference on Communication and Electronics Systems (ICCES), Coimbatore, Indian."},{"key":"ref_14","unstructured":"Takens, F. (2006). Detecting Strange Attractors in Turbulence, Springer. Dynamical Systems and Turbulence, Warwick 1980: Proceedings of a Symposium Held at the University of Warwick 1979\/80."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"2138","DOI":"10.1109\/78.942641","article-title":"Reconstructions and predictions of nonlinear dynamical systems: A hierarchical Bayesian approach","volume":"49","author":"Matsumoto","year":"2001","journal-title":"IEEE Trans. Signal Process."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"3333","DOI":"10.1109\/TIT.2005.853308","article-title":"On the Jensen-Shannon divergence and variational distance","volume":"51","author":"Tsai","year":"2005","journal-title":"IEEE Trans. Inf. Theory"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"9821","DOI":"10.1109\/JSTARS.2022.3218055","article-title":"Sea Clutter Suppression Based on Complex-Valued Neural Networks Optimized by PSD","volume":"15","author":"Zhu","year":"2022","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"43","DOI":"10.1016\/S0167-2789(97)00118-8","article-title":"Practical method for determining the minimum embedding dimension of a scalar time series","volume":"110","author":"Cao","year":"1997","journal-title":"Phys. D Nonlinear Phenom."},{"key":"ref_19","first-page":"656","article-title":"Sea detecting X-band radar and data acquisition program","volume":"8","author":"Liu","year":"2019","journal-title":"J. Radars"},{"key":"ref_20","first-page":"173","article-title":"Annual Progress of Sea-detecting X-band Radar and Data Acquisition Program","volume":"10","author":"Liu","year":"2021","journal-title":"J. Radars"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"331","DOI":"10.1016\/j.stamet.2006.11.004","article-title":"An R-square coefficient based on final prediction error","volume":"4","author":"Rousson","year":"2007","journal-title":"Stat. Methodol."},{"key":"ref_22","unstructured":"Drosopoulos, A. (1994). Description of the OHGR Database, National Defence Canada, Defence Research Establishment Ottawa. Tech. Note."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"1502","DOI":"10.1109\/TAES.2010.5545205","article-title":"Impact of Sea Clutter Nonstationarity on Disturbance Covariance Matrix Estimation and CFAR Detector Performance","volume":"46","author":"Greco","year":"2010","journal-title":"IEEE Trans. Aerosp. Electron. Syst."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/16\/7\/1260\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T14:22:38Z","timestamp":1760106158000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/16\/7\/1260"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,4,2]]},"references-count":23,"journal-issue":{"issue":"7","published-online":{"date-parts":[[2024,4]]}},"alternative-id":["rs16071260"],"URL":"https:\/\/doi.org\/10.3390\/rs16071260","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,4,2]]}}}