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Sea clutter is studied and analyzed for evaluating the accuracy of missile-borne synthetic aperture radar under different sea conditions. A parameter estimation method based on BP neural network is proposed to solve the problem that the sea clutter parameter estimation based on traditional statistical methods is prone to be divorced from the actual sea clutter. The amplitude distribution characteristics and temporal correlation of sea clutter are used to establish a K-distribution-based sea clutter model. The influences of four model parameters, such as shape, scale, mean square root of clutter speed, and mean Doppler shift, on sea clutter chaos and fractal characteristics are analyzed, and the qualitative relationship among model parameters and physical characteristics is summarized. On this basis, BP neural network is used to fully explore the quantitative relationship among parameters and physical characteristics, and predict the chaos characteristics and fractal characteristics. The determinate coefficients are 0.985 and 0.952. The model parameters of BP neural network, maximum likelihood estimation and moment estimation method are compared by taking the measured sea clutter data as an example. The results show that the proposed model can be well close to the physical characteristics of actual sea clutter. [ABSTRACT FROM AUTHOR]

A time-variant curve (TVC) model-based automatic equalization method is proposed for the intensity variation problem of synthetic aperture sonar (SAS) images. A theoretical expression of TVC is deduced based on sound transmission model, underwater backscattering strength model and SAS image model. A method to calculate the TVC observations is established based on the statistical model of SAS images. An estimation method based on non-linear least square fitting model (NL-LSFM) is used to acquire the optimized parameter of TVC. At last, the SAS images are automatically equalized and enhanced based on the optimized TVC. The method proposed has been validated by lake and sea trials. The test results show that the result calculated by the TVC expression is consistent with the experimental data, and the automatic equalization method can remove the intensity variation of SAS images properly. [ABSTRACT FROM AUTHOR]