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We resort to a Bayesian framework and design knowledge-aided (KA) Rao and Wald tests for radar adaptive detection based on the partially homogeneous model, which outperform their conventional counterparts in heterogeneous environment. Meanwhile, the coincidence of the proposed detectors is proved.
This paper deals with the adaptive detection of a signal of interest in the presence of Gaussian noise with unknown covariance matrix (CM). To this end, we resort to a Bayesian approach based on a suitable model for the probability density function (PDF) of unknown CM. Under this assumption, the maximum a-posteriori (MAP) estimation of CM is derived. The MAP estimate is in turn used to yield Bayesian...
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