R
Renbiao Wu
Researcher at Civil Aviation University of China
Publications - 156
Citations - 1545
Renbiao Wu is an academic researcher from Civil Aviation University of China. The author has contributed to research in topics: Radar & Clutter. The author has an hindex of 17, co-authored 156 publications receiving 1420 citations. Previous affiliations of Renbiao Wu include Xidian University & University of Florida.
Papers
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Time-delay- and time-reversal-based robust capon beamformers for ultrasound imaging
Zhisong Wang,Jian Li,Renbiao Wu +2 more
TL;DR: This paper considers a recent promising robust Capon beamformer (RCB), which restores the appeal of SCB including its high resolution and superb interference suppression capabilities, and also retains the attractiveness of DAS including its robustness against steering vector errors.
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An efficient algorithm for time delay estimation
Jian Li,Renbiao Wu +1 more
TL;DR: The WRELAX method is a relaxation-based minimizer of a complicated nonlinear least squares criterion and can be applied to detecting and classifying roadway subsurface anomalies by using an ultra-wideband ground-penetrating radar.
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Microwave imaging via adaptive beamforming methods for breast cancer detection
TL;DR: Two data-adaptive methods for microwave imaging are developed, which are referred to as the robust weighted Capon beamforming (RWCB) method and the amplitude and phase estimation (APES) method, which outperform their data-independent counterparts in terms of improved resolution and reduced sidelobe levels.
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Robust autofocus algorithm for ISAR imaging of moving targets
TL;DR: Numerical and experimental results have shown that AUTOCLEAN is a very robust autofocus tool for ISAR imaging.
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Super resolution time delay estimation via MODE-WRELAX
Renbiao Wu,Jian Li,Zheng-She Liu +2 more
TL;DR: In this article, the authors proposed an efficient time delay estimation method based on WRELAX (Weighted Fourier transform and relaxation based) algorithm, which can be used for both complex and real-valued signals with highly oscillatory correlation functions.