A novel parameter estimation for the compound gaussian sea clutter model with the inverse gamma texture based on logarithmic moment derivative

The compound Gaussian model with the inverse Gamma texture is composed of fast-changing speckle and slow-changing texture components. It can accurately model the long trailing distribution characteristics under the high-state sea clutter environment. In this paper, we present a novel parameter estimation method for the compound Gaussian model with the inverse Gamma texture based on the logarithmic moment derivative. In this method, the expressions of the shape and scale parameters of the compound Gaussian model with the inverse Gamma texture are derived by using the logarithmic moment and its derivative. Finally, the optimization algorithm is used to achieve the efficient and high precision estimations of shape and scale parameters, beneficial for the subsequent target CFAR detection. Both simulation and real-measured airborne sea clutter data verify the effectiveness of the proposed algorithm.