In this paper, we propose a new numerical method for an efficient unbiased estimator for the K-plus-noise distribution model parameters for sea clutter intensity in thermal noise. The method estimates the shape parameter and the clutterto-noise ratio (CNR) using log-based intensity moments and two-dimensional (2-D) constrained nonlinear moment matching. Using simulated stationary sea clutter intensity data, we demonstrate that the proposed estimator is asymptotically efficient as its performance approaches the Cramér-Rao lower bound (CRLB). We also compare its performance with other moment and curve fitting estimation methods, using real sea clutter observations.
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