In this paper, we propose two adaptive decision schemes with enhanced detection and range estimation capabilities for point-like targets buried in Gaussian clutter with unknown spectral properties. To this end, we jointly exploit the spillover of target energy to consecutive range samples and a Bayesian framework where the clutter covariance matrix is modeled as a random matrix obeying the inverse complex Wishart distribution. Under the above assumptions, two Bayesian detectors based on the Generalized Likelihood Ratio Test (GLRT) and the so-called two-step GLRT design procedure are derived. It is shown that the proposed detectors are modified versions of the traditional localization detectors through a colored-loading step utilizing the a priori knowledge and the sample covariance matrix. Finally, performance assessment, conducted on both simulated data and real recorded datasets, demonstrates the effectiveness of the proposed detectors compared with their non-Bayesian counterparts.
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