Adaptive detection of distributed targets in 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 Gaussian disturbance without secondary data. Suppose that some apriori spectral models for the interference in the cells under test and a lower bound on the power spectral density (PSD) of the white disturbance term 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 Gaussian disturbance as well as their superiority with respect to some existing techniques.