Adaptive detection based on multiple a-priori spectral models for MIMO radar in compound-Gaussian clutter

In this paper, we consider the adaptive detection with multiple-input multiple-output (MIMO) radar in the presence of compound-Gaussian clutter with a limited number of secondary data set. We assume that multiple a-priori spectral models for the clutter are available, and model the actual clutter inverse covariance structure as a combination of these available a-priori models. In this framework, a sequential optimization algorithm is first presented to estimate the unknown parameters. Then, an approximate generalized likelihood ratio test (GLRT) is developed by exploiting the obtained estimates. Finally, we evaluate the capabilities of the proposed detector against compound-Gaussian clutter as well as its superiority with respect to some existing techniques with few secondary data support.