Abstract
In this paper, we focus on the effective Space-Time Adaptive Processing (STAP) method in nonhomogeneous clutter environment. The nonhomogeneous clutter leads to the lack of sufficient training data for clutter covariance matrix estimation in traditional STAP methods. By utilizing the sparsity of the distribution of clutter in angle-Doppler domain, we build a factor graph model and develop a message passing algorithm to estimate the space-time distribution of clutter. The proposed method effectively reduces the number of training data compared with traditional methods. The numerical results show that the method outperforms the existing sparse recovery based STAP methods in nonhomogeneous clutter environment with higher accuracy and lower complexity.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: 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.