Abstract
In conventional statistical STAP algorithms, the existence of interference target in training samples will lead to signal cancellation, resulting in the output SCR falling and the moving target detection performance degrading. The nonhomogeneity detector is an efiective way to restrain the outlier, which can improve the covariance matrix estimation by detecting the samples containing outliers and rejecting them, and improve the STAP performance. A new interference target detection algorithm is proposed in this paper, the outlier detection is realized by using the samples' data phase information. Compared with traditional method, the improved algorithm is more sensitive to interfering target with difierent azimuth and intensity. Simulation results demonstrate the validity of this improved method.
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.
