pplication of nonlinear predictor for sea clutter was restricted to sea clutter chaotic model .This paper is concerned with the application of a nonlinear predictor for sea clutter modeled statistically, using a zero–memory nonlinearity (ZMNL) followed by a second order Volterra filter(SVF) as described in [2 First partial statistical information such as marginal probability density function (PDF)and the covariance structure are exploited .The clutter PDF parameters are estimated by a combinational method based on maximum likelihood and method of moments ,resulting in the lowest variance of parameter estimation as mentioned in [5]. The ZMNL transformes the process into a Gaussian process, finally the transformed process is predicted by a SVF. The improvement concerned with application of the nonlinear predictor when compared with linear one is outlined
