Bayesian-based parameter estimation of K distribution using method of logarithmic cumulants

In this paper, the problem of using the method of logarithmic cumulants (MoLC) for parameter estimation of the K distribution is addressed. Specifically, we have pointed out that the MoLC is likely to suffer from non-invertible equations. In order to overcome such difficulty, a prior distribution is introduced to the estimated term in the log-cumulant equation and closed-form Bayesian estimation is obtained. Numerical experiments demonstrate that this approach not only provides an always-solvable equation, but also universally improves the estimation accuracy. Finally, the application of the MoLC for ship detection in synthetic aperture radar (SAR) images is demonstrated. Experimental results with the RADARSAT-2 data show that the proposed method leads to better sea clutter modeling in terms of more accurate constant false alarm rate (CFAR) control.