{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,8]],"date-time":"2026-06-08T00:34:37Z","timestamp":1780878877753,"version":"3.54.1"},"reference-count":16,"publisher":"MDPI AG","issue":"13","license":[{"start":{"date-parts":[[2022,6,24]],"date-time":"2022-06-24T00:00:00Z","timestamp":1656028800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>In this paper, a criterion for sea surface zoning based on the fractal characteristics of disturbances is demonstrated. To improve radar detection performance in heavy-tailed sea clutter, a multitude of sea clutter models and corresponding optimum and suboptimum detectors have been designed in recent years. However, there are cases where these models and detectors become insufficient to describe the coexistence of noise and sea clutter. The commonly used signal-to-clutter ratio (SCR) can hardly serve as an indicator revealing which kind of disturbances dominate in a certain area since it is difficult to decide the level of SCR at which sea clutter or noise exceeds the other. Therefore, it is necessary that a set of rules reflecting essential differences between sea clutter and noise are proposed to tell areas where sea clutter dominates, areas where sea clutter and noise coexist and areas where noise dominates. Analyzing fractal characteristics of disturbances, we consider the Hurst exponent H as a feature distinguishing sea clutter and noise from each other. A modified Sigmoid function is employed to model the variation in H with range bins, and the derivative of the function helps to formulate a set of rules for sea surface zoning.<\/jats:p>","DOI":"10.3390\/s22134761","type":"journal-article","created":{"date-parts":[[2022,6,23]],"date-time":"2022-06-23T22:43:00Z","timestamp":1656024180000},"page":"4761","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["A Sea-Surface-Zoning Method Based on Fractal Characteristics"],"prefix":"10.3390","volume":"22","author":[{"given":"Huaxing","family":"Kuang","sequence":"first","affiliation":[{"name":"School of Information Science and Engineering, Southeast University, Nanjing 211189, China"},{"name":"Nanjing Marine Radar Institute, Nanjing 211153, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Luxi","family":"Yang","sequence":"additional","affiliation":[{"name":"School of Information Science and Engineering, Southeast University, Nanjing 211189, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,6,24]]},"reference":[{"key":"ref_1","first-page":"102","article-title":"Radar sea-clutter at low grazing angles","volume":"137","author":"Chan","year":"1990","journal-title":"IEE Proc. F"},{"key":"ref_2","first-page":"51","article-title":"Maritime surveillance radar. Part I: Radar scattering from the ocean surface","volume":"137","author":"Ward","year":"1990","journal-title":"IEE Proc. F"},{"key":"ref_3","first-page":"39","article-title":"Coherent radar detection in log-normal clutter","volume":"133","author":"Farina","year":"1986","journal-title":"IEE Proc."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"806","DOI":"10.1109\/TAP.1976.1141451","article-title":"A model for non-Rayleigh sea echo","volume":"24","author":"Jakeman","year":"1976","journal-title":"IEEE Trans. Antennas Propag."},{"key":"ref_5","first-page":"80","article-title":"Canadian east coast radar sea trials and the K-distribution","volume":"138","author":"Nohara","year":"1991","journal-title":"IEE Proc. F"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Ward, K., Tough, R., and Watts, S. (2013). Sea Clutter: Scattering, the K distribution and Radar Performance, Institute of Engineering Technology.","DOI":"10.1049\/PBRA025E"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"648","DOI":"10.1109\/36.581981","article-title":"A model for extremely heterogeneous clutter","volume":"35","author":"Frery","year":"1997","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"1572","DOI":"10.1109\/LSP.2016.2605129","article-title":"Iterative maximum likelihood and outlier-robust bipercentile estimation of parameters of compound-Gaussian clutter with inverse Gaussian texture","volume":"23","author":"Shui","year":"2016","journal-title":"IEEE Signal Process. Lett."},{"key":"ref_9","first-page":"109","article-title":"Development of optimum coherent detection in compound-Gaussian sea clutter models","volume":"35","author":"Han","year":"2017","journal-title":"Sci. Technol. Rev."},{"key":"ref_10","first-page":"366","article-title":"Land-sea separation and sea surface zoning algorithms for sea surface target","volume":"8","author":"Ming","year":"2019","journal-title":"J. Radars"},{"key":"ref_11","unstructured":"Mandelbrot, B. (1982). Fractals Form, Chance, and Dimension, Freeman W H."},{"key":"ref_12","first-page":"243","article-title":"Fractal characterisation of sea-scattered signals and detection of sea-surface targets","volume":"140","author":"Lo","year":"1990","journal-title":"IEE Proc. F"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"1131","DOI":"10.1364\/JOSAA.7.001131","article-title":"Scattering from fractally corrugated surfaces","volume":"7","author":"Jaggard","year":"1990","journal-title":"J. Opt. Soc. Am. A"},{"key":"ref_14","unstructured":"Guan, J., Liu, N., and Huang, Y. (2011). Fractal Theory and Applications for Radar Target Detection, Publishing House of Electronics Industry."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"164","DOI":"10.1090\/qam\/10666","article-title":"A method for the solution of certain non-linear problems in least squares","volume":"2","author":"Levenberg","year":"1944","journal-title":"Q. Appl. Math."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"431","DOI":"10.1137\/0111030","article-title":"An algorithm for least-squares estimation of nonlinear parameters","volume":"11","author":"Marquardt","year":"1963","journal-title":"SIAM J. Appl. Math."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/13\/4761\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T23:39:01Z","timestamp":1760139541000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/13\/4761"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,6,24]]},"references-count":16,"journal-issue":{"issue":"13","published-online":{"date-parts":[[2022,7]]}},"alternative-id":["s22134761"],"URL":"https:\/\/doi.org\/10.3390\/s22134761","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,6,24]]}}}