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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An Improved SRGAN Based Ambiguity Suppression Algorithm for SAR Ship Target Contrast Enhancement
TL;DR: The proposed improved super-resolution generative adversarial network (ISRGAN) based ambiguity suppression algorithm for SAR ship target contrast enhancement is the first attempt of using GAN for SAR ambiguity suppression.
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Micro-Doppler Feature Extraction of Inverse Synthetic Aperture Imaging Laser Radar Using Singular-Spectrum Analysis.
TL;DR: A micro-Doppler feature extraction algorithm is proposed for the inverse synthetic aperture imaging laser radar (ISAIL) and Singular-spectrum analysis (SSA) is employed for separation and reconstruction of the micro- doppler and rigid body signal.
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A two-dimensional phase coding for range ambiguity suppression
Jian Li,Guang-Cai Sun,Xiang-Gen Xia,Xiang-Gen Xia,Liang Guo,Jing Guobin,Mengdao Xing,Zheng Bao +7 more
TL;DR: Based on a two-dimensional phase coding, a novel range ambiguity suppression technique is proposed, which suppresses the range ambiguity by transmitting two- dimensional phase coded signals.
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Data-Driven Motion Compensation for Airborne Bistatic SAR Imagery Under Fast Factorized Back Projection Framework
TL;DR: The practical problem of unexpected motion errors of the airborne platform is carefully analyzed under a fast factorized back-projection (FFBP) framework for a general BiSAR process and a coherent data-driven motion compensation (MOCO) algorithm integrated with FFBP is proposed.
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SMOTE-Based Weighted Deep Rotation Forest for the Imbalanced Hyperspectral Data Classification
TL;DR: In this paper, a non-ANN based deep learning, namely SMOTE-Based Weighted Deep Rotation Forest (SMOTE-WDRoF), is proposed to solve the problem of imbalance hyperspectral data classification.