Knowledge-aided adaptive subspace detection in partially homogeneous environments

Abstract

In this paper, we consider the 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, which is controlled by the parameters of the statistics distribution of the CCM. Based on the Bayesian framework, a knowledge-aided adaptive subspace detector (KA-ASD) is given, and can be used to detect the subspace signal in partially homogeneous environments. The computer simulation is used to validate that KA-ASD is outperform the conventional subspace detector, and especially within a small number of training samples and coherent pulses.