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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...
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...
We present the problem of moving target detection with multiple-input multiple-output (MIMO) radar in compound-Gaussian clutter. A new moving target detector based on the Wald criterion is devised according to the centralized processing scheme of MIMO radar system. Furthermore, the adaptive version of the proposed detector is investigated by in place of the exact covariance matrix with the fixed point...
This paper considers moving target detection for multiple-input multiple-output (MIMO) radar in compound-Gaussian clutter. A new detector is devised according to a centralized processing scheme for MIMO radar system based on the generalized likelihood ratio test (GLRT) design criterion. Then, an adaptive version of the derived detector is investigated. The fixed point estimation (FPE) strategy is...
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