Skip to Main Content
In this paper, we consider an adaptive subspace detector for partially homogeneous environments. In this environment, the clutter covariance matrix (CCM) of secondary data is equal to the CCM of the cell under test (CUT), except for a real constant factor. We also suppose that we have some prior knowledge of the CCM, and the prior knowledge is controlled by the parameters of the statistical distribution of the CCM. Based on the Bayesian framework, a knowledge-aided adaptive subspace detector (KA-ASD) is given, which can be used to detect the subspace signal in partially environments. Computer simulation is used to validate that KA-ASD outperforms the conventional subspace detector, especially in situations with a small number of secondary data.