Covariance matrix tapers estimation in knowledge-aided STAP for sea clutter environment

In knowledge-aided space-time adaptive processing (KASTAP), a priori knowledge of the clutter covariance matrix is incorporated to enhance the radar detection performance in heterogeneous environment. In KA-STAP under sea clutter environment, the internal clutter motion (ICM) cannot be neglected, so it is necessary to estimate the covariance matrix taper (CMT) of the a priori covariance matrix according to real time clutter state. Traditionally, CMT is modelled as a singer function, which is not suitable in sea clutter environment for its lacking of adaptability. In this paper, we develop a CMT directly data estimation approach (DDE), where ICM information is extracted from training samples and used to construct CMT. By utilizing real time extracted information, the DDE CMT approach has the ability to cognitive time-varying sea clutter environment. Simulation results verify the effectiveness of the proposed CMT estimation approach.