Adaptive detection of distributed targets in compound-Gaussian clutter without secondary data: An approach based on multiple a-priori spectral models

In this paper, we consider the problem of adaptive detection for distributed targets embedded in compoundGaussian clutter without secondary data. Suppose that some a-priori spectral models for the clutter in the cells under test are available. First, we propose an approximate estimate algorithm for the unknown parameters based on a heuristic approach under both the hypothesis. Then, an approximate Generalized Likelihood Ratio Test (GLRT) is proposed by exploiting the heuristic estimates. Finally, we evaluate the capabilities of the proposed detector against compound-Gaussian clutter as well as their superiority with respect to some existing techniques.