A Novel Parameter Estimation Method for the Family of G Distribution

A novel parameter estimation method for the family of G distribution is proposed. This paper first analyzes the limitations of the common method of moments (MoM) for estimating the parameters of the family of G distribution. Then, based on the analysis, a fast and robust method of parameter estimation for the family of G distribution based on the Mellin transform is presented. The novel estimation method has the following advantages: firstly, it solves the problems of MoM used to estimate the parameters of the family of G distribution; secondly, it regards the number of looks as a parameter to be estimated, like the other parameters; thirdly, the parameter estimation values can be quickly and accurately acquired, all of which guarantee the family of G distribution’s fitting precision. According to the experiments performed on different clutter areas, with the Kullback-Leibler (KL) distance, mean square error (MSE) and Kolmogorov-Smirnov (KS) test as similarity measurements, the proposed estimators show better fitting performance than the MoM estimators.