{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,3]],"date-time":"2026-09-03T15:51:04Z","timestamp":1788450664909,"version":"build-2803163510"},"reference-count":39,"publisher":"MDPI AG","issue":"23","license":[{"start":{"date-parts":[[2023,12,4]],"date-time":"2023-12-04T00:00:00Z","timestamp":1701648000000},"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":["62101260"],"award-info":[{"award-number":["62101260"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61001229"],"award-info":[{"award-number":["61001229"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62101264"],"award-info":[{"award-number":["62101264"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["2020Z441"],"award-info":[{"award-number":["2020Z441"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Jiangsu Postdoctoral Foundation","award":["62101260"],"award-info":[{"award-number":["62101260"]}]},{"name":"Jiangsu Postdoctoral Foundation","award":["61001229"],"award-info":[{"award-number":["61001229"]}]},{"name":"Jiangsu Postdoctoral Foundation","award":["62101264"],"award-info":[{"award-number":["62101264"]}]},{"name":"Jiangsu Postdoctoral Foundation","award":["2020Z441"],"award-info":[{"award-number":["2020Z441"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>In recent years, super-resolution imaging techniques have been intensely introduced to enhance the azimuth resolution of real aperture scanning radar (RASR). However, there is a paucity of research on the subject of sea surface imaging with small incident angles for complex scenarios. This research endeavors to explore super-resolution imaging for sea surface monitoring, with a specific emphasis on grounded or shipborne platforms. To tackle the inescapable interference of sea clutter, it was segregated from the imaging objects and was modeled alongside I\/Q channel noise within the maximum likelihood framework, thus mitigating clutter\u2019s impact. Simultaneously, for characterizing the non-stationary regions of the monitoring scene, we harnessed the Markov random field (MRF) model for its two-dimensional (2D) spatial representational capacity, augmented by a quadratic term to bolster outlier resilience. Subsequently, the maximum a posteriori (MAP) criterion was employed to unite the ML function with the statistical model regarding imaging scene. This hybrid model forms the core of our super-resolution methodology. Finally, a fast iterative threshold shrinkage method was applied to solve this objective function, yielding stable estimates of the monitored scene. Through the validation of simulation and real data experiments, the superiority of the proposed approach in recovering the monitoring scenes and clutter suppression has been verified.<\/jats:p>","DOI":"10.3390\/s23239609","type":"journal-article","created":{"date-parts":[[2023,12,4]],"date-time":"2023-12-04T07:59:31Z","timestamp":1701676771000},"page":"9609","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Real Aperture Radar Super-Resolution Imaging for Sea Surface Monitoring Based on a Hybrid Model"],"prefix":"10.3390","volume":"23","author":[{"given":"Ke","family":"Tan","sequence":"first","affiliation":[{"name":"School of Electronic and Optical Engineering, Nanjing University of Science and Technology, Nanjing 210094, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shengqi","family":"Zhou","sequence":"additional","affiliation":[{"name":"School of Electronic and Optical Engineering, Nanjing University of Science and Technology, Nanjing 210094, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xingyu","family":"Lu","sequence":"additional","affiliation":[{"name":"School of Electronic and Optical Engineering, Nanjing University of Science and Technology, Nanjing 210094, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jianchao","family":"Yang","sequence":"additional","affiliation":[{"name":"School of Electronic and Optical Engineering, Nanjing University of Science and Technology, Nanjing 210094, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Weimin","family":"Su","sequence":"additional","affiliation":[{"name":"School of Electronic and Optical Engineering, Nanjing University of Science and Technology, Nanjing 210094, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hong","family":"Gu","sequence":"additional","affiliation":[{"name":"School of Electronic and Optical Engineering, Nanjing University of Science and Technology, Nanjing 210094, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,12,4]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"421","DOI":"10.1175\/1520-0426(2001)018<0421:SARART>2.0.CO;2","article-title":"SWIMSAT: A real-aperture radar to measure directional spectra of ocean waves from space\u2014Main characteristics and performance simulation","volume":"18","author":"Hauser","year":"2001","journal-title":"J. Atmos. Ocean. Tech."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Ly, C., Dropkin, H., and Manitius, A.Z. (2002, January 30). Extension of the music algorithm to millimeter-wave (mmw) real-beam radar scanning antennas. Proceedings of the Radar Sensor Technology and Data Visualization, Orlando, FL, USA.","DOI":"10.1117\/12.488280"},{"key":"ref_3","unstructured":"Chen, L., Jiang, X., Huang, P., Zhang, Y., and Liu, X. (August, January 28). Efficient nonconvex regularization for azimuth resolution enhancement of real beam scanning radar. Proceedings of the IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Yokohama, Japan."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"121","DOI":"10.1007\/BF01212466","article-title":"Radar beam sharpening using an optimum FIR filter","volume":"19","author":"Sadjadi","year":"2000","journal-title":"Circ. Syst. Sig. Proces."},{"key":"ref_5","unstructured":"Richards, M.A. (1988, January 20\u201321). Iterative noncoherent angular superresolution (radar). Proceedings of the IEEE National Radar Conference, Arbor, MI, USA."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"912","DOI":"10.1049\/rsn2.12229","article-title":"Super-resolution forward-looking imaging method for manoeuvering platform with optimised dictionary and extended sparsity adaptive matching pursuit","volume":"16","author":"Zhang","year":"2022","journal-title":"IET Rad. Son. Navi."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"4218","DOI":"10.1109\/JSTARS.2023.3270309","article-title":"Radar Forward-Looking Super-Resolution Imaging Using a Two-Step Regularization Strategy","volume":"16","author":"Tuo","year":"2023","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Huo, W., Zhang, Q., Zhang, Y., Zhang, Y., Huang, Y., and Yang, J. (2021). A superfast super-resolution method for radar forward-looking imaging. IEEE Sensors, 21.","DOI":"10.3390\/s21030817"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/TGRS.2023.3329887","article-title":"A Computationally efficient airborne forward-looking super-Resolution imaging method based on sparse bayesian Learning","volume":"61","author":"Li","year":"2023","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Zhang, J., Wu, D., Zhu, D., and Jiang, P. (2017, January 14\u201316). An airborne\/missile-borne array radar forward-looking imaging algorithm based on super-resolution method. Proceedings of the 10th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI), Shanghai, China.","DOI":"10.1109\/CISP-BMEI.2017.8302131"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"566","DOI":"10.1049\/el.2017.0324","article-title":"Li, M. Knowledge-aided mono-pulse forward-looking imaging for airborne radar by exploiting the antenna pattern information","volume":"53","author":"Chen","year":"2017","journal-title":"Electron. Lett."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"2044","DOI":"10.1109\/JSTARS.2019.2920859","article-title":"Airborne forward-looking radar super-resolution imaging using iterative adaptive approach","volume":"12","author":"Zhang","year":"2019","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Guo, Y., Zhang, D., Li, Q., Zhang, P., and Liang, Y. (2022, January 2\u20134). FIAA-Based Super-Resolution Forward-Looking Radar Imaging Method for Maneuvering Platforms. Proceedings of the 3rd China International SAR Symposium (CISS), Shanghai, China.","DOI":"10.1109\/CISS57580.2022.9971231"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"6609","DOI":"10.1109\/TGRS.2020.3027053","article-title":"Fast sparse-TSVD super-resolution method of real aperture radar forward-looking imaging","volume":"59","author":"Tuo","year":"2020","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_15","unstructured":"Shu, Z., Zong, Z., Huang, L., and Huang, L. (August, January 28). Forward-looking radar super-resolution imaging combined TSVD with l1 norm constraint. Proceedings of the IEEE International Geoscience and Remote Sensing Symposium, Yokohama, Japan."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"1027","DOI":"10.1088\/0266-5611\/21\/3\/014","article-title":"Tikhonov regularization applied to the inverse problem of option pricing: Convergence analysis and rates","volume":"21","author":"Egger","year":"2005","journal-title":"Inv. Prob."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"573","DOI":"10.1109\/83.913592","article-title":"A computationally efficient superresolution image reconstruction algorithm","volume":"10","author":"Nguyen","year":"2001","journal-title":"IEEE Trans. Image Process"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"863","DOI":"10.1049\/el.2014.3978","article-title":"Sparse super-resolution imaging for airborne single channel forward-looking radar in expanded beam space via lp regularisation","volume":"51","author":"Chen","year":"2015","journal-title":"Electron. Lett."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Han, J., Zhang, S., Zheng, S., Wang, M., Ding, H., and Yan, Q. (2022). Bias Analysis and Correction for Ill-Posed Inversion Problem with Sparsity Regularization Based on L1 Norm for Azimuth Super-Resolution of Radar Forward-Looking Imaging. Remote Sens., 14.","DOI":"10.3390\/rs14225792"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Zhang, Q., Zhang, Y., Zhang, Y., Huang, Y., and Yang, J. (2021). Airborne radar super-resolution imaging based on fast total variation method. Remote Sens., 13.","DOI":"10.3390\/rs13040549"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"6534","DOI":"10.1109\/TGRS.2020.2977719","article-title":"TV-sparse super-resolution method for radar forward-looking imaging","volume":"58","author":"Zhang","year":"2020","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/TGRS.2020.3034752","article-title":"Bayesian forward-looking superresolution imaging using Doppler deconvolution in expanded beam space for high-speed platform","volume":"60","author":"Chen","year":"2021","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Tan, K., Lu, X., Yang, J., Su, W., and Gu, H. (2021). A novel Bayesian super-resolution method for radar forward-looking imaging based on Markov random field model. Remote Sens., 13.","DOI":"10.3390\/rs13204115"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"1093203","DOI":"10.3389\/frsip.2023.1093203","article-title":"Data-driven airborne bayesian forward-looking superresolution imaging based on generalized Gaussian distribution","volume":"3","author":"Chen","year":"2023","journal-title":"Front. Signal Process."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Zhang, Y., Huang, Y., and Yang, J. (2017). A sparse Bayesian approach for forward-looking superresolution radar imaging. Sensors, 17.","DOI":"10.3390\/s17061353"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Tan, K., Li, W., Zhang, Q., Huang, Y., Wu, J., and Yang, J. (2018). Penalized maximum likelihood angular super-resolution method for scanning radar forward-looking imaging. Sensors, 18.","DOI":"10.3390\/s18030912"},{"key":"ref_27","first-page":"1","article-title":"Real aperture radar forward-looking imaging based on variational Bayesian in presence of outliers","volume":"60","author":"Li","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"2822","DOI":"10.1109\/JSTARS.2019.2918189","article-title":"Sea-surface target angular superresolution in forward-looking radar imaging based on maximum a posteriori algorithm","volume":"12","author":"Zhang","year":"2019","journal-title":"IEEE J. Sel. Top. Appl. Earth Observ. Remote Sens."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"13419","DOI":"10.1109\/ACCESS.2020.2965973","article-title":"A Bayesian angular superresolution method with lognormal constraint for sea-surface target","volume":"8","author":"Yang","year":"2020","journal-title":"IEEE Access"},{"key":"ref_30","first-page":"1","article-title":"Scanning radar forward-looking superresolution imaging based on the Weibull distribution for a sea-surface target","volume":"60","author":"Zhang","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Li, W., Li, M., Zuo, L., Sun, H., Chen, H., and Li, Y. (2021). Forward-looking super-resolution imaging for sea-surface target with multi-prior Bayesian method. Remote Sens., 14.","DOI":"10.3390\/rs14010026"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"121","DOI":"10.1049\/el.2011.2774","article-title":"Keystone transform-based space-variant range migration correction for airborne forward-looking scanning radar","volume":"48","author":"Li","year":"2012","journal-title":"Electron. Lett."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"146","DOI":"10.1049\/iet-rsn.2009.0132","article-title":"Use of sea clutter models in radar design and development","volume":"4","author":"Ward","year":"2010","journal-title":"IET Radar Sonar Navig."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"180","DOI":"10.1002\/tee.23284","article-title":"Detection of target and suppression of sea and weather clutter in stormy weather by Weibull\/CFAR","volume":"16","author":"Sayama","year":"2010","journal-title":"IEEJ Trans. Electr. Electron. Eng."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"903","DOI":"10.1109\/TAES.2004.1337463","article-title":"Statistical analysis of real clutter at different range resolutions","volume":"40","author":"Conte","year":"2004","journal-title":"IEEE Trans. Aerosp. Electron. Syst."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"952","DOI":"10.1109\/JSTARS.2011.2179524","article-title":"Markov random field models for non-quadratic regularization of complex SAR images","volume":"5","author":"Gleich","year":"2012","journal-title":"IEEE J. Sel. Top. Appl. Earth Observ. Remote Sens."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"1487","DOI":"10.1038\/s41598-021-91871-3","article-title":"Computation of distribution of relaxation times by Tikhonov regularization for Li ion batteries: Usage of L-curve method","volume":"11","author":"Paul","year":"2021","journal-title":"Sci. Rep."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"968","DOI":"10.1007\/s10957-015-0746-4","article-title":"On the convergence of the iterates of the fast iterative shrinkage\/thresholding algorithm","volume":"166","author":"Chambolle","year":"2015","journal-title":"J. Opt. Theor. Appl."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"106","DOI":"10.1109\/7.366297","article-title":"Computer generation of correlated non-Gaussian radar clutter","volume":"31","author":"Rangaswamy","year":"1995","journal-title":"IEEE Trans. Aerosp. Electron. Syst."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/23\/9609\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T21:37:40Z","timestamp":1760132260000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/23\/9609"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,12,4]]},"references-count":39,"journal-issue":{"issue":"23","published-online":{"date-parts":[[2023,12]]}},"alternative-id":["s23239609"],"URL":"https:\/\/doi.org\/10.3390\/s23239609","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,12,4]]}}}