Two-step Bayesian detection for MIMO radar in compound-Gaussian clutter with Gamma texture

Abstract

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 Gamma distribution and the structure is drawn from an inverse complex Wishart distribution. In this framework, an adaptive generalized likelihood ratio test (GLRT) is developed using two-step design procedure. Precisely, we first obtain the GLRT by assuming the known clutter structure. Then, we derive the maximum a posteriori (MAP) estimator of the structure, and substitute it into the obtained GLRT. Finally, we evaluate the capabilities of the proposed detector against compound-Gaussian clutter as well as their superiority with respect to some existing techniques.