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A Low Sample Size Estimator for K Distributed Noise

Author(s): Eduardo X. Alban | Mario E. Magaña | Harry Skinner

Journal: Journal of Signal and Information Processing
ISSN 2159-4465

Volume: 03;
Issue: 03;
Start page: 293;
Date: 2012;
Original page

Keywords: Estimation | K Distribution | Mean Square Error | Moments

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.
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