Abstract
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.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
