A Low Sample Size Estimator for K Distributed Noise

Eduardo X. Alban, Mario E. Magaña, Harry Skinner

Intel Corporation, Hillsboro, USA.
Oregon State University.

DOI: 10.4236/jsip.2012.33039                

Abstract

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.

Keywords

Estimation; K Distribution; Mean Square Error; Moments

Conflicts of Interest

The authors declare no conflicts of interest.

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