P
Pu Wang
Researcher at Mitsubishi Electric Research Laboratories
Publications - 144
Citations - 2659
Pu Wang is an academic researcher from Mitsubishi Electric Research Laboratories. The author has contributed to research in topics: Parametric statistics & Estimator. The author has an hindex of 22, co-authored 126 publications receiving 2099 citations. Previous affiliations of Pu Wang include Chalmers University of Technology & Stevens Institute of Technology.
Papers
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Multiantenna-Assisted Spectrum Sensing for Cognitive Radio
TL;DR: The results show that the proposed GLRT exhibits better performance than other existing techniques, particularly when the number of samples is small, which is particularly critical in vehicular applications.
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Pattern-Coupled Sparse Bayesian Learning for Recovery of Block-Sparse Signals
TL;DR: A new sparse Bayesian learning method for recovery of block-sparse signals with unknown cluster patterns by introducing a pattern-coupled hierarchical Gaussian prior to characterize the pattern dependencies among neighboring coefficients, where a set of hyperparameters are employed to control the sparsity of signal coefficients.
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Moving Target Detection Using Distributed MIMO Radar in Clutter With Nonhomogeneous Power
Pu Wang,Hongbin Li,Braham Himed +2 more
TL;DR: A generalized-likelihood ratio test (GLRT) for moving target detection in distributed MIMO radar is developed and shown to be a constant false alarm rate (CFAR) detector and the test statistic is a central and noncentral Beta variable under the null and alternative hypotheses, respectively.
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Integrated Cubic Phase Function for Linear FM Signal Analysis
TL;DR: In this paper, an integrated cubic phase function (ICPF) is introduced for the estimation and detection of linear frequency-modulated (LFM) signals, which extends the standard CPF to handle cases involving low signal-to-noise ratio (SNR) and multi-component LFM signals.
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Millimeter Wave Channel Estimation via Exploiting Joint Sparse and Low-Rank Structures
TL;DR: In this paper, a two-stage compressed sensing method for mmWave channel estimation is proposed, where the sparse and low-rank properties are respectively utilized in two consecutive stages, namely, a matrix completion stage and a sparse recovery stage.