Results 11 to 20 of about 7,085,552 (171)
Sparse Bayesian learning-based massive multi-user detection algorithm [PDF]
Tongxin xuebao, 2023 Aiming at the problem that most existing algorithms were based on the Gaussian inverse gamma prior model (GIG-SBL), which ignored the sparsity of the support set vector within the sparse solution, a sparse Bayesian learning framework based on the ...
Pingping CHEN +4 more
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Complex Multisnapshot Sparse Bayesian Learning for Offgrid DOA Estimation
International Journal of Antennas and Propagation, 2022 Direction of arrival (DOA) estimation has recently been developed based on sparse signal reconstruction (SSR). Sparse Bayesian learning (SBL) is a typical method of SSR.
Qinghua Liu +3 more
doaj +2 more sources
IEEE Transactions on Signal Processing, 2022 Abstract Hybrid precoding in millimeter-wave large MIMO systems is a critical component for lowering radio-frequency hardware costs as compared to the traditional fulldigital precoding strategy. Knowledge of channels is required for hybrid precoding. Though, estimation of the channel is problematic for the mm-Wave system because of the usage of
Suraj Srivastava +4 more
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Underwater Acoustic Channel Estimation Based on Sparse Bayesian Learning Algorithm
IEEE Access, 2023 The channel estimation algorithm based on sparse Bayesian learning proposed in recent years shows better performance than the traditional channel estimation algorithm by effectively reducing the convergence error in the channel estimation process ...
Shuyang Jia +3 more
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IET Radar, Sonar & Navigation, 2022 Sparse recovery algorithms have been applied to the Space‐time adaptive processing for reducing the requirement of samples over the past 15 years. However, many Sparse recovery algorithms are not robust and need accurate user parameters.
Cheng Liu +3 more
doaj +1 more source
Transactions of the JSME (in Japanese), 2022 Spatiotemporal local changes have been extracted for evaluating physiological phenomena by an image reconstruction algorithm of electrical impedance tomography (EIT) using sparse Bayesian learning (SBL). The proposed method identifies a region of interest (ROI) by a priori information on conductivity distribution σ of each biological tissue and ...
Ryoma OGAWA +2 more
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, 2023 In Millimeter-Wave (mm-Wave) massive Multiple-Input Multiple-Output (MIMO) systems, hybrid precoders/combiners must be designed to improve antenna gain and reduce hardware complexity.
Khmaies Ouahada (16954681) +4 more
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Frequency Analysis of Acoustic Data Using Multiple-Measurement Sparse Bayesian Learning
Sensors, 2021 Passive sonar systems are used to detect the acoustic signals that are radiated from marine objects (e.g., surface ships, submarines, etc.), and an accurate estimation of the frequency components is crucial to the target detection.
Myoungin Shin +3 more
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Identification of nonlinear sparse networks using sparse Bayesian learning [PDF]
, 2017 © 2017 IEEE. This paper considers a parametric approach to infer sparse networks described by nonlinear ARX models, with linear ARX treated as a special case. The proposed method infers both the Boolean structure and the internal dynamics of the network.
Webb, AA +6 more
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An Efficient Sparse Bayesian Learning STAP Algorithm with Adaptive Laplace Prior
Remote Sensing, 2022 Space-time adaptive processing (STAP) encounters severe performance degradation with insufficient training samples in inhomogeneous environments. Sparse Bayesian learning (SBL) algorithms have attracted extensive attention because of their robust and ...
Weichen Cui +3 more
doaj +1 more source