Knowledge-aided Bayesian detection for MIMO radar in compound-Gaussian clutter with inverse Gamma texture

In this paper, we consider the adaptive detection with multiple-input multiple-output (MIMO) radar in compound-Gaussian clutter. The covariance matrices of the primary and the secondary data share a common structure but different power levels (textures). A Bayesian framework is exploited where both the textures and the structure are assumed to be random. Precisely, the textures follow inverse Gamma distribution and the structure is drawn from an inverse complex Wishart distribution. In this framework, the generalized likelihood ratio test (GLRT) is derived. Finally, we evaluate the capabilities of the proposed detector against compound-Gaussian clutter as well as their superiority with respect to some existing techniques.