Maximum likelihood estimation of K-distribution parameters via the expectation-maximization algorithm

Authors:
W.J.J. Roberts;S. Furui
Affiliations:
Inf. Technol. Div., Defence Sci. & Technol. Organ., Salisbury, SA, Australia;-
Venue:
IEEE Transactions on Signal Processing
Year:
2000

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Signal Processing

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Abstract

Maximum likelihood (ML) estimates of K-distribution parameters are derived using the expectation maximization (EM) approach. This approach demonstrates the computational advantages compared with 2-D numerical maximization of the likelihood function using a Nelder-Mead approach. For large datasets, the EM approach yields more accurate estimates than those of a non-ML estimation technique.