Generalization of the KA distribution is formulated by combining the class A and K distributions; the resulting distribution is termed as generalized KA distribution. It is obtained as a mixture of a generalized Rayleigh and a class A distribution with gamma-distributed mean intensity, and it may be used to describe clutter statistics. Its parameters are estimated by implementing the expectation maximization algorithm. The latter provides estimates in the framework of the maximum likelihood principle, and it is widely used when the data set is incomplete and/or of limited size. The numerical results show that the absolute relative error of the estimated parameters may be <8 % even in the case of 100 data samples, whereas it is reduced significantly as the size of the data set increases.