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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A High-Order Phase Correction Approach for Focusing HS-SAR Small-Aperture Data of High-Speed Moving Platforms
TL;DR: A high-order phase correction approach (HPCA) combined with SPECAN operation for focusing high-squint SAR (HS-SAR) small-aperture data is developed and has been successfully used to focus the real airborne radar data recently.
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The Space-Variant Phase-Error Matching Map-Drift Algorithm for Highly Squinted SAR
TL;DR: This letter proposes a space-variant phase-error matching MD algorithm that can improve the precision of estimating the Doppler chirp rate for the highly squinted SAR by removing the influence of the azimuthal position-dependent phases.
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Integration of Rotation Estimation and High-Order Compensation for Ultrahigh-Resolution Microwave Photonic ISAR Imagery
TL;DR: A UHR MWP-ISAR imaging algorithm integrating rotation estimation and high-order motion terms compensation is proposed, and extensive experiments demonstrate that the proposed algorithm outperforms traditional ISAR imaging strategies in high- order RCM correction and azimuth focusing performance.
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Processing of Bistatic SAR Data With Nonlinear Trajectory Using a Controlled-SVD Algorithm
TL;DR: In this paper, a nonlinear trajectory SAR imaging algorithm based on controlled singular value decomposition (CSVD) is proposed to improve the image quality compared with SVD-Stolt.
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Semi-supervised rotation forest based on ensemble margin theory for the classification of hyperspectral image with limited training data
Wei Feng,Yinghui Quan,Gabriel Dauphin,Qiang Li,Lianru Gao,Wenjiang Huang,Junshi Xia,Wentao Zhu,Mengdao Xing +8 more
TL;DR: An adaptive semi-supervised rotation forest (SSRoF) algorithm is proposed for the classification of hyperspectral images with limited training data, based on Rotation Forest, a classifying technique that has proved to be remarkably accurate in the context of high-dimensional data.