Adaptive Bayesian detection for multiple-input multiple-output radar in compound-Gaussian clutter with random texture


During this study, the authors take into account the adaptive detection with multiple-input multiple-output radar in compound-Gaussian clutter. The covariance matrices of the primary and therefore the secondary information share a standard structure, however different power levels (textures). A Bayesian framework is exploited where each the textures and also the structure are assumed to be random. Precisely, the textures follow Gamma distribution or inverse Gamma distribution and also the structure is drawn from an inverse complex Wishart distribution. During this framework, 2 generalised chance ratio tests are derived. Finally, they evaluate the capabilities of the proposed detectors against compound-Gaussian clutter as well as their superiority with respect to some existing techniques.

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