An Empirical Model of Shape Parameter of Sea Clutter Based on X-Band Island-Based Radar Database

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Abstract
Sea clutter modelling and parameter estimation/prediction are important basis of system design, performance assessment, and target detection of maritime radars. Besides parameter estimation from measured data, parameter prediction from radar parameters, sea state, and the viewing geometry of the radar is an alternative way. In this letter, a dual-polarimetric full-recorded sea clutter database measured by an island-based X-band experimental radar on the Yellow sea of China is introduced. On this database, the model selection reveals that the compound-Gaussian model with inverse Gaussian (CGIG) texture is suited for HH high-resolution sea clutter data, the generalized Pareto (GP) intensity distributions are for HH moderate-resolution data, and the K amplitude distributions are for high and moderate-resolution VV polarized data. Further, three five-parametric empirical formulae are constructed to predict the shape parameter of the CGIG, GP, and K distributions of sea clutter from the area of spatial resolution cell, the grazing angle, the significant wave height, the average wave period, and the wave direction relative to the sight line of radar. The new empirical formulae attain more accurate prediction than existing empirical formula on the database.
Funding Information
  • National Natural Science Foundation of China (62071346)

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