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Guoan Bi
Researcher at Nanyang Technological University
Publications - 335
Citations - 4372
Guoan Bi is an academic researcher from Nanyang Technological University. The author has contributed to research in topics: Radar imaging & Inverse synthetic aperture radar. The author has an hindex of 30, co-authored 335 publications receiving 3703 citations. Previous affiliations of Guoan Bi include Hangzhou Normal University & University of Surrey.
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Lv's Distribution: Principle, Implementation, Properties, and Performance
TL;DR: The distribution is demonstrated to be a CFCR representation that is computed without using any searching operation and to generate a new TF representation, called inverse LVD (ILVD), and a new ambiguity function, called Lv's ambiguity function (LVAF), both of which may break through the tradeoff between resolution and cross terms.
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A Survey on Protograph LDPC Codes and Their Applications
TL;DR: This paper provides a comprehensive survey on the state-of-the-art in protograph LDPC code design and analysis for different channel conditions, including the additive white Gaussian noise channels, fading channels, partial response channels, and Poisson pulse-position modulation channels.
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An Autofocus Technique for High-Resolution Inverse Synthetic Aperture Radar Imagery
TL;DR: Experimental results based on synthetic and practical data have demonstrated that the proposed algorithm has a desirable denoising capability and can produce a relatively well-focused image of the target, particularly in low signal-to-noise ratio and high undersampling ratio scenarios, compared with other recently reported methods.
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Enhanced ISAR Imaging by Exploiting the Continuity of the Target Scene
TL;DR: A novel inverse synthetic aperture radar (ISAR) imaging method by exploiting the inherent continuity of the scatterers on the target scene to obtain enhanced target images within a Bayesian framework that can achieve substantial improvements in the scenarios of limited measurements and low signal-to-noise ratio compared with other reported algorithms for ISAR imaging problems.
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Novel Wideband DOA Estimation Based on Sparse Bayesian Learning With Dirichlet Process Priors
TL;DR: A novel wideband DOA estimation algorithm is proposed to simultaneously infer the band occupation and estimate high-resolution DOAs by leveraging the sparsity in the angular domain.