A Low Sample Size Estimator for K Distributed Noise
Eduardo X. Alban, Mario Edgardo Magana, Harry G. Skinner · Journal of Signal and Information Processing · 2012
In this paper, we derive a new method for estimating the parameters of the K-distribution when a limited number of samples are available. The method is based on an approximation of the Bessel function of the second kind that reduces the complexity of the estimation formulas in comparison to those used by the maximum likelihood algorithm. The proposed method has better performance in comparison with existing methods of the same complexity giving a lower mean squared error when the number of samples used for the estimation is relatively low.