Results 31 to 40 of about 7,085,552 (171)
SPP-SBL: Space-Power Prior Sparse Bayesian Learning for Block Sparse Recovery
IEEE Transactions on Signal ProcessingThe recovery of block-sparse signals with unknown structural patterns remains a fundamental challenge in structured sparse signal reconstruction. By proposing a variance transformation framework, this paper unifies existing pattern-based block sparse Bayesian learning methods, and introduces a novel space power prior based on undirected graph models to
Yanhao Zhang, Zhihan Zhu, Yong Xia 0002
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Sparse Bayesian Learning Using Approximate Message Passing
, 2020 -We use the approximate message passing framework (AMP) [1] to address the problem of recovering a sparse vector from undersampled noisy measurements.
Maher Al-Shoukairi, Bhaskar Rao
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IEEE Access, 2022 Sparse Bayesian learning (SBL) has been extended to estimate acoustic channel impulse responses (CIRs) at low frequencies, where matched filter (MF)-based CIR estimation suffers from low resolution due to a limited frequency band.
Youngmin Choo, Haesang Yang
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Correlated Sparse Bayesian Learning for Recovery of Block Sparse Signals With Unknown Borders
IEEE Open Journal of Signal ProcessingWe consider the problem of recovering complex-valued block sparse signals with unknown borders. Such signals arise naturally in numerous applications. Several algorithms have been developed to solve the problem of unknown block partitions.
Didem Dogan, Geert Leus
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Advanced Photonics Research, Volume 7, Issue 4, April 2026.This review chronicles the evolution of intelligent electromagnetic (EM) perception systems empowered by metasurfaces and artificial intelligence. The progression from data‐driven imaging with implicit priors to semantic sensing with explicit priors is systematically reviewed.
Xingrui Zhou +5 more
wiley +1 more source
CoFAR Clutter Channel Estimation via Sparse Bayesian Learning [PDF]
, 2023 peer reviewedA cognitive fully adaptive radar (CoFAR) alters its behavior autonomously to accomplish desired tasks. The knowledge of the target environment is essential to the efficient operation of CoFAR.
OTTERSTEN, Björn +2 more
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Seasonal Decomposition‐Enhanced Deep Learning Architecture for Probabilistic Forecasting
Journal of Forecasting, Volume 45, Issue 2, Page 880-891, March 2026.ABSTRACT Time series decomposition as a general preprocessing method has been widely used in the field of time series forecasting. However, since the future is unknown, this preprocessing practice is limited in realistic forecasting, especially in real‐time forecasting scenarios.
Keyan Jin +1 more
wiley +1 more source
IET Communications, Volume 20, Issue 1, January/December 2026.This paper addresses channel estimation in mmWave (millimetre wave) hybrid MIMO (multiple‐input multiple‐output) systems impaired by residual transceiver hardware nonidealities, modelled as additive non‐Gaussian noise via Bussgang decomposition. A hyperparameter‐free maximum Versoria criterion (MVC)‐based channel estimator is proposed, featuring a ...
Rangeet Mitra +5 more
wiley +1 more source
IEEE Access, 2018 This paper concerns the problem of sparse signal recovery with multiple measurement vectors, where the sparse signal vectors share multiple supports (i.e., the signal vectors can be clustered and the vectors in a cluster share a common support) and the ...
Zhengdao Yuan +5 more
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Leida xuebao, 2022 The direct position determination method based on compressed sensing depends on the accurate signal propagation model. With partially unknown propagation model parameters, its location performance will decline significantly.
Hongzhen YE +4 more
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