Knowledge-based adaptive detection of radar targets in generalized Pareto clutter

This paper studies adaptive detection of radar targets embedded in generalized Pareto clutter on the condition with the limited secondary data. In order to alleviate the effects of the non-Gaussian characteristic of the clutter, a-priori knowledge of the non-Gaussian clutter is considered in the designed detector. More precisely, we consider that the texture of clutter obeys the inverse gamma distribution and the inverse covariance matrix of speckle is a combination of multiple a-priori spectral models. Within these considerations, we obtain an adaptive detector based on the generalized likelihood ratio test. Finally, the performance of the proposed detector is evaluated via the Monte-Carlo technique. The experiments results indicate that the proposed detector outperforms the 1S-GLRT detector in limited secondary data scenarios.