M
Mengdao Xing
Researcher at Xidian University
Publications - 549
Citations - 10244
Mengdao Xing is an academic researcher from Xidian University. The author has contributed to research in topics: Synthetic aperture radar & Radar imaging. The author has an hindex of 44, co-authored 471 publications receiving 7300 citations. Previous affiliations of Mengdao Xing include Chinese Academy of Sciences.
Papers
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Proceedings ArticleDOI
Systematic analyses of challenges and solutions in geosynchronous synthetic aperture radar
TL;DR: In this paper, the GEOSAR has been studied through analyses of bi-static imaging geometry, signal model, Doppler characteristics, doppler bandwidth and synthetic aperture time, and resolution evaluation of the GE OSAR imagery, in which both challenges and solutions are included.
Journal ArticleDOI
SAR platform positioning method based on improved Gauss–Newton–genetic hybrid algorithm
TL;DR: A new robust SAR platform positioning method based on the improved Gauss–Newton–genetic hybrid (GNGH) algorithm that can reduce the computation load and positioning error dramatically and is confirmed and demonstrated via simulation and experimental results.
Journal ArticleDOI
A Novel Clutter Covariance Matrix Estimation Method Based on Feature Subspace for Space-Based Early Warning Radar
TL;DR: In this article, a method based on clutter subspace reconstruction and spectrum correction technology can improve the estimation accuracy of clutter covariance matrix in the case of nonstationary signals and heterogeneous environments.
Journal ArticleDOI
GF-3 data real-time processing method based on multi-satellite distributed data processing system
TL;DR: A multi-satellite distributed SAR real-time processing method based on Chirp Scaling (CS) imaging algorithm is studied in this paper, and a distributed data processing system is built with field programmable gate array (FPGA) chips as the kernel.
Journal ArticleDOI
Parameter estimation of QFM signal based on MPKF
TL;DR: A novel method for the parameter estimation of multi-component QFM signal based on a modified product kernel function (MPKF) that has fewer external cross-terms and light computational burden because of non-iterative operations and is robust against additive noise is proposed.