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Journal articles on the topic 'Sparse Bayesian learning (SBL)'

Author: Grafiati

Published: 24 June 2021

Last updated: 29 July 2025

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1

Yuan, Cheng, and Mingjun Su. "Seismic spectral sparse reflectivity inversion based on SBL-EM: experimental analysis and application." Journal of Geophysics and Engineering 16, no. 6 (2019): 1124–38. http://dx.doi.org/10.1093/jge/gxz082.

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Abstract In this paper, we propose a new method of seismic spectral sparse reflectivity inversion that, for the first time, introduces Expectation-Maximization-based sparse Bayesian learning (SBL-EM) to enhance the accuracy of stratal reflectivity estimation based on the frequency spectrum of seismic reflection data. Compared with the widely applied sequential algorithm-based sparse Bayesian learning (SBL-SA), SBL-EM is more robust to data noise and, generally, can not only find a sparse solution with higher precision, but also yield a better lateral continuity along the final profile. To inve

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2

Liu, Qinghua, Yuanxin He, Kai Ding, and Quanmin Xie. "Complex Multisnapshot Sparse Bayesian Learning for Offgrid DOA Estimation." International Journal of Antennas and Propagation 2022 (February 28, 2022): 1–12. http://dx.doi.org/10.1155/2022/4500243.

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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. In SBL, the two-layer hierarchical model in Gaussian scale mixtures (GSMs) has been used to model sparsity-inducing priors. However, this model is mainly applied to real-valued signal models. In order to apply SBL to complex-valued signal models, a general class of sparsity-inducing priors is proposed for complex-valued signal models by complex Gaussian scale mixtures (CGSMs), and the special cases correspond to complex versio

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Shin, Myoungin, Wooyoung Hong, Keunhwa Lee, and Youngmin Choo. "Frequency Analysis of Acoustic Data Using Multiple-Measurement Sparse Bayesian Learning." Sensors 21, no. 17 (2021): 5827. http://dx.doi.org/10.3390/s21175827.

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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. In this paper, we introduce sparse Bayesian learning (SBL) for the frequency analysis after the corresponding linear system is established. Many algorithms, such as fast Fourier transform (FFT), estimate signal parameters via rotational invariance techniques (ESPRIT), and multiple signal classification (RMUSIC) has been proposed for frequency detection. However, the

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Liu, Kun, Tong Wang, Jianxin Wu, Cheng Liu, and Weichen Cui. "On the Efficient Implementation of Sparse Bayesian Learning-Based STAP Algorithms." Remote Sensing 14, no. 16 (2022): 3931. http://dx.doi.org/10.3390/rs14163931.

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Sparse Bayesian learning-based space–time adaptive processing (SBL-STAP) algorithms can achieve superior clutter suppression performance with limited training sample support in practical heterogeneous and non-stationary clutter environments. However, when the system has high degrees of freedom (DOFs), SBL-STAP algorithms suffer from high computational complexity, since the large-scale matrix calculations and the inversion operations of large-scale covariance matrices are involved in the iterative process. In this article, we consider a computationally efficient implementation for SBL-STAP algo

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Shin, Myoungin, Wooyoung Hong, Keunhwa Lee, and Youngmin Choo. "Passive Sonar Target Identification Using Multiple-Measurement Sparse Bayesian Learning." Sensors 22, no. 21 (2022): 8511. http://dx.doi.org/10.3390/s22218511.

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Accurate estimation of the frequency component is an important issue to identify and track marine objects (e.g., surface ship, submarine, etc.). In general, a passive sonar system consists of a sensor array, and each sensor receives data that have common information of the target signal. In this paper, we consider multiple-measurement sparse Bayesian learning (MM-SBL), which reconstructs sparse solutions in a linear system using Bayesian frameworks, to detect the common frequency components received by each sensor. In addition, the direction of arrival estimation was performed on each detected

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Wang, Hongyan, Yanping Bai, Jing Ren, et al. "DOA Estimation Method for Vector Hydrophones Based on Sparse Bayesian Learning." Sensors 24, no. 19 (2024): 6439. http://dx.doi.org/10.3390/s24196439.

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Through extensive literature review, it has been found that sparse Bayesian learning (SBL) is mainly applied to traditional scalar hydrophones and is rarely applied to vector hydrophones. This article proposes a direction of arrival (DOA) estimation method for vector hydrophones based on SBL (Vector-SBL). Firstly, vector hydrophones capture both sound pressure and particle velocity, enabling the acquisition of multidimensional sound field information. Secondly, SBL accurately reconstructs the received vector signal, addressing challenges like low signal-to-noise ratio (SNR), limited snapshots,

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Ali K., Shoukath, Arfat Ahmad Khan, Perarasi T, Ateeq Ur Rehman, and Khmaies Ouahada. "Learned-SBL-GAMP based hybrid precoders/combiners in millimeter wave massive MIMO systems." PLOS ONE 18, no. 9 (2023): e0289868. http://dx.doi.org/10.1371/journal.pone.0289868.

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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. Sparse Bayesian learning via Expectation Maximization (SBL-EM) algorithm is not practically feasible for high signal dimensions because estimating sparse signals and designing optimal hybrid precoders/combiners using SBL-EM still provide high computational complexity for higher signal dimensions. To overcome the issues of high computational complexity along with making it suitable for larger data sets, in this pa

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Wang, Jinyang, El-Bay Bourennane, Mahdi Madani, et al. "High-Throughput MPSoC Implementation of Sparse Bayesian Learning Algorithm." Electronics 13, no. 1 (2024): 234. http://dx.doi.org/10.3390/electronics13010234.

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In the field of sparse signal reconstruction, sparse Bayesian learning (SBL) has excellent performance, which is accompanied by extremely high computational complexity. This paper presents an efficient SBL hardware and software (HW&SW) co-implementation method using the ZYNQ series MPSoC (multiprocessor system-on-chip). Firstly, considering the inherent challenges in parallelizing iterative algorithms like SBL, we propose an architecture based on the iterative calculations implemented on the PL side (FPGA) and the iteration control and input management handled by the PS side (ARM). By adop

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Cui, Weichen, Tong Wang, Degen Wang, and Kun Liu. "An Efficient Sparse Bayesian Learning STAP Algorithm with Adaptive Laplace Prior." Remote Sensing 14, no. 15 (2022): 3520. http://dx.doi.org/10.3390/rs14153520.

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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 self-regularizing nature. In this study, a computationally efficient SBL STAP algorithm with adaptive Laplace prior is developed. Firstly, a hierarchical Bayesian model with adaptive Laplace prior for complex-value space-time snapshots (CALM-SBL) is formulated. Laplace prior enforces the sparsity more heavily than Gaussian, which achieves a better

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Cho, Yong-Ho. "Fast Sparse Bayesian Learning-Based Channel Estimation for Underwater Acoustic OFDM Systems." Applied Sciences 12, no. 19 (2022): 10175. http://dx.doi.org/10.3390/app121910175.

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Harsh underwater channels and energy constraints are the two critical issues of underwater acoustic (UWA) communications. To achieve a high channel estimation performance under a severe underwater channel, sparse Bayesian learning (SBL)-based channel estimation was adopted for UWA orthogonal frequency division multiplexing (OFDM) systems. Accurate channel estimation can guarantee the successful reception of transmitted data and reduce retransmission occurrences, thereby, leading to energy-efficient communications. However, SBL-based algorithms have improved performances in iterative ways, whic

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Niu, Haiqiang, and Peter Gerstoft. "Normal mode extraction using sparse Bayesian learning in shallow water." Journal of the Acoustical Society of America 152, no. 4 (2022): A267. http://dx.doi.org/10.1121/10.0016232.

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The low-frequency signals propagating in the shallow-water waveguides are dispersive. They are composed of several normal modes according to the normal mode theory. For the vertical array data, the horizontal wavenumbers and the associated multi-frequency modal depth functions were estimated using block sparse Bayesian learning ( Niu et al., JASA 2020), while a priori knowledge of sea bottom, moving source, and source locations is not needed. For the impulsive or known-form signals received by one hydrophone, the sparse Bayesian learning (SBL) approach can be also used to extract the modes ( N

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Bi, Chuan-Xing, Feng-Min Zhang, Xiao-Zheng Zhang, Yong-Bin Zhang, and Rong Zhou. "Sound field reconstruction using block sparse Bayesian learning equivalent source method." Journal of the Acoustical Society of America 151, no. 4 (2022): 2378–90. http://dx.doi.org/10.1121/10.0010103.

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Nearfield acoustic holography based on the compressed sensing theory can realize the accurate reconstruction of sound fields with fewer measurement points on the premise that an appropriate sparse basis is obtained. However, for different types of sound sources, the appropriate sparse bases are diverse and should be constructed elaborately. In this paper, a block sparse Bayesian learning (SBL) equivalent source method is proposed for realizing the reconstruction of the sound fields radiated by different types of sources, including the spatially sparse sources, the spatially extended sources, a

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NYEO, SU-LONG, and RAFAT R. ANSARI. "EARLY CATARACT DETECTION BY DYNAMIC LIGHT SCATTERING WITH SPARSE BAYESIAN LEARNING." Journal of Innovative Optical Health Sciences 02, no. 03 (2009): 303–13. http://dx.doi.org/10.1142/s1793545809000632.

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Dynamic light scattering (DLS) is a promising technique for early cataract detection and for studying cataractogenesis. A novel probabilistic analysis tool, the sparse Bayesian learning (SBL) algorithm, is described for reconstructing the most-probable size distribution of α-crystallin and their aggregates in an ocular lens from the DLS data. The performance of the algorithm is evaluated by analyzing simulated correlation data from known distributions and DLS data from the ocular lenses of a fetal calf, a Rhesus monkey, and a man, so as to establish the required efficiency of the SBL algorithm

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Li, Taiyong, Zhenda Hu, Yanchi Jia, Jiang Wu, and Yingrui Zhou. "Forecasting Crude Oil Prices Using Ensemble Empirical Mode Decomposition and Sparse Bayesian Learning." Energies 11, no. 7 (2018): 1882. http://dx.doi.org/10.3390/en11071882.

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Crude oil is one of the most important types of energy and its prices have a great impact on the global economy. Therefore, forecasting crude oil prices accurately is an essential task for investors, governments, enterprises and even researchers. However, due to the extreme nonlinearity and nonstationarity of crude oil prices, it is a challenging task for the traditional methodologies of time series forecasting to handle it. To address this issue, in this paper, we propose a novel approach that incorporates ensemble empirical mode decomposition (EEMD), sparse Bayesian learning (SBL), and addit

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Wang, Jia, Lanyue Zhang, Bo Hu, Di Wu, and Xueru Hu. "Sparse Bayesian learning based on spatio-temporal structure-aware for matched field processing." Journal of the Acoustical Society of America 155, no. 1 (2024): 328–42. http://dx.doi.org/10.1121/10.0024352.

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In the application of the matched field processing (MFP) algorithm for underwater acoustic source localization, the measurements at each time step are conventionally processed independently. This study incorporates the prior information about the continuous spatial changes of the source over time under realistic conditions, a factor anticipated to improve localization performance. In this paper, a sparse Bayesian learning (SBL) algorithm based on the spatio-temporal structure-aware is described. We exploit a structure prior for sparse coefficients to capture the continuous spatial structure be

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Ming, Chao, Haiqiang Niu, Zhenglin Li, and Yu Wang. "Passive synthetic aperture for direction-of-arrival estimation using sparse Bayesian learning." Journal of the Acoustical Society of America 153, no. 4 (2023): 2061–72. http://dx.doi.org/10.1121/10.0017785.

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Passive synthetic aperture (PSA) extension for a moving array has the ability to enhance the accuracy of direction-of-arrival (DOA) estimation by constructing a larger virtual aperture. The array element overlap in array continuous measurements is required for the traditional extended towed array measurement (ETAM) methods. Otherwise, the phase factor estimation is biased, and the aperture extension fails when multiple sources exist. To solve this problem, passive aperture extension with sparse Bayesian learning (SBL) is proposed. In this method, SBL is used to simultaneously estimate the phas

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Zhang, Bing, Rui Jin, Longyu Jiang, Lei Yang, and Tao Zhang. "Robust Sparse Bayesian Learning Source Localization in an Uncertain Shallow-Water Waveguide." Electronics 13, no. 23 (2024): 4789. https://doi.org/10.3390/electronics13234789.

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Conventional matched-field processing (MFP) for acoustic source localization is sensitive to environmental mismatches because it is based on the wave propagation model and environmental information that is uncertain in reality. In this paper, a mode-predictable sparse Bayesian learning (MPR-SBL) method is proposed to increase robustness in the presence of environmental uncertainty. The estimator maximizes the marginalized probability density function (PDF) of the received data at the sensors, utilizing the Bayesian rule and two hyperparameters (the source powers and the noise variance). The re

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18

Wang, Ran, Xiaoquan Yi, Liang Yu, Chenyu Zhang, Tongdong Wang, and Xiaopeng Zhang. "Infrasound Source Localization of Distributed Stations Using Sparse Bayesian Learning and Bayesian Information Fusion." Remote Sensing 14, no. 13 (2022): 3181. http://dx.doi.org/10.3390/rs14133181.

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The precise localization of the infrasound source is important for infrasound event monitoring. The localization of infrasound sources is influenced by the atmospheric propagation environment and infrasound measurement equipment in the large-scale global distribution of infrasound arrays. A distributed infrasound source localization method based on sparse Bayesian learning (SBL) and Bayesian information fusion is proposed to reduce the localization error. First, the arrival azimuth of the infrasound source is obtained based on the SBL algorithm. Then, the infrasound source localization result

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Huang, Jianli, Yu Wang, Zaixiao Gong, Jun Wang, and Haibin Wang. "Fast deconvolved beamforming For arbitrary arrays based on beam-domain sparse Bayesian learning." Journal of the Acoustical Society of America 155, no. 3_Supplement (2024): A88. http://dx.doi.org/10.1121/10.0026906.

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Aiming at the problem that the conventional deconvolved beamforming methods cannot be directly applied to the specific array with a shift-variant point spread function and also have considerable computational workload, a deconvolved beamforming method for arbitrary arrays based on beam-domain sparse Bayesian learning(SBL) is proposed. First, generalized convolution model for arbitrary arrays in beam-domain is derived. Conventional beamforming is used to obtain several complex output beams. Then, to improve the accuracy of direction of arrival(DOA) estimation, the off-grid SBL method which adop

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Liu, Qi, Xianpeng Wang, Mengxing Huang, Xiang Lan, and Lu Sun. "DOA and Range Estimation for FDA-MIMO Radar with Sparse Bayesian Learning." Remote Sensing 13, no. 13 (2021): 2553. http://dx.doi.org/10.3390/rs13132553.

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Due to grid division, the existing target localization algorithms based on sparse signal recovery for the frequency diverse array multiple-input multiple-output (FDA-MIMO) radar not only suffer from high computational complexity but also encounter significant estimation performance degradation caused by off-grid gaps. To tackle the aforementioned problems, an effective off-grid Sparse Bayesian Learning (SBL) method is proposed in this paper, which enables the calculation the direction of arrival (DOA) and range estimates. First of all, the angle-dependent component is split by reconstructing t

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Pan, Kaikai, Zheng Qian, and Niya Chen. "Probabilistic Short-Term Wind Power Forecasting Using Sparse Bayesian Learning and NWP." Mathematical Problems in Engineering 2015 (2015): 1–11. http://dx.doi.org/10.1155/2015/785215.

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Probabilistic short-term wind power forecasting is greatly significant for the operation of wind power scheduling and the reliability of power system. In this paper, an approach based on Sparse Bayesian Learning (SBL) and Numerical Weather Prediction (NWP) for probabilistic wind power forecasting in the horizon of 1–24 hours was investigated. In the modeling process, first, the wind speed data from NWP results was corrected, and then the SBL was used to build a relationship between the combined data and the power generation to produce probabilistic power forecasts. Furthermore, in each model,

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Jin, Wenzhe, Wentao Lyu, Yingrou Chen, Qing Guo, Zhijiang Deng, and Weiqiang Xu. "Efficient Sparse Bayesian Learning Model for Image Reconstruction Based on Laplacian Hierarchical Priors and GAMP." Electronics 13, no. 15 (2024): 3038. http://dx.doi.org/10.3390/electronics13153038.

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In this paper, we present a novel sparse Bayesian learning (SBL) method for image reconstruction. We integrate the generalized approximate message passing (GAMP) algorithm and Laplacian hierarchical priors (LHP) into a basic SBL model (called LHP-GAMP-SBL) to improve the reconstruction efficiency. In our SBL model, the GAMP structure is used to estimate the mean and variance without matrix inversion in the E-step, while LHP is used to update the hyperparameters in the M-step.The combination of these two structures further deepens the hierarchical structures of the model. The representation abi

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Wang, Ju, Bingqi Shan, Song Duan, Yi Zhao, and Yi Zhong. "An Off-Grid Compressive Sensing Algorithm Based on Sparse Bayesian Learning for RFPA Radar." Remote Sensing 16, no. 2 (2024): 403. http://dx.doi.org/10.3390/rs16020403.

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In the application of Compressive Sensing (CS) theory for sidelobe suppression in Random Frequency and Pulse Repetition Interval Agile (RFPA) radar, the off−grid issues affect the performance of target parameter estimation in RFPA radar. Therefore, to address this issue, this paper presents an off−grid CS algorithm named Refinement and Generalized Double Pareto (GDP) distribution based on Sparse Bayesian Learning (RGDP−SBL) for RFPA radar that utilizes a coarse−to−fine grid refinement approach, allowing precise and cost−effective signal recovery while mitigating the impact of off−grid issues o

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Li, Xionghui, Guolong Liang, Tongsheng Shen, and Zailei Luo. "Velocity Estimation of Passive Target Based on Sparse Bayesian Learning Cross-Spectrum." Sensors 24, no. 21 (2024): 6989. http://dx.doi.org/10.3390/s24216989.

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To solve the poor performance or even failure of the cross-spectrum (CS) method in hydroacoustic weak-target passive velocimetry, a sparse Bayesian learning cross-spectrum method (SBL-CS), combining phase compensation with sparse Bayesian learning (SBL) is proposed in this paper. Firstly, the cross-correlation sound intensity is taken as the observation quantity and compensates for each frequency point of the cross-spectrum, which enables the alignment of cross-spectrum results at different frequencies. Then, the inter-correlation sound intensity of all frequencies is fused in the iterative es

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Dong, Hang, Fengzhou Dai, and Juan Zhang. "High-Speed Target HRRP Reconstruction Based on Fast Mean-Field Sparse Bayesian Unrolled Network." Remote Sensing 17, no. 1 (2024): 8. https://doi.org/10.3390/rs17010008.

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The rapid and accurate reconstruction of the high-resolution range profiles (HRRPs) of high-speed targets from incomplete wideband radar echoes is a critical component in space target recognition tasks (STRTs). However, state-of-the-art HRRP reconstruction algorithms based on sparse Bayesian learning (SBL) are computationally expensive and require the manual selection of prior scale parameters. To address these challenges, this paper proposes a model-driven deep network based on fast mean-field SBL (FMFSBL-Net) for the HRRP reconstruction of high-speed targets under missing data conditions. Sp

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Li, Ninghui, Xiaokuan Zhang, Fan Lv, and Binfeng Zong. "Direction-of-Arrival Estimation via Sparse Bayesian Learning Exploiting Hierarchical Priors with Low Complexity." Sensors 24, no. 7 (2024): 2336. http://dx.doi.org/10.3390/s24072336.

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For direction-of-arrival (DOA) estimation problems in a sparse domain, sparse Bayesian learning (SBL) is highly favored by researchers owing to its excellent estimation performance. However, traditional SBL-based methods always assign Gaussian priors to parameters to be solved, leading to moderate sparse signal recovery (SSR) effects. The reason is Gaussian priors play a similar role to l2 regularization in sparsity constraint. Therefore, numerous methods are developed by adopting hierarchical priors that are used to perform better than Gaussian priors. However, these methods are in straitened

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Gerstoft, Peter, Christoph Mecklenbrauker, Santosh Nannuru, and Geert Leus. "DOA Estimation in Heteroscedastic Noise with sparse Bayesian Learning." Applied Computational Electromagnetics Society 35, no. 11 (2021): 1439–40. http://dx.doi.org/10.47037/2020.aces.j.351188.

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We consider direction of arrival (DOA) estimation from long-term observations in a noisy environment. In such an environment the noise source might evolve, causing the stationary models to fail. Therefore a heteroscedastic Gaussian noise model is introduced where the variance can vary across observations and sensors. The source amplitudes are assumed independent zero-mean complex Gaussian distributed with unknown variances (i.e., source powers), leading to stochastic maximum likelihood (ML) DOA estimation. The DOAs are estimated from multi-snapshot array data using sparse Bayesian learning (SB

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Gao, Zhiqi, Na Yang, Pingping Huang, Wei Xu, Weixian Tan, and Zhixia Wu. "Self-Calibrating STAP Algorithm for Dictionary Dimensionality Reduction Based on Sparse Bayesian Learning." Electronics 14, no. 12 (2025): 2350. https://doi.org/10.3390/electronics14122350.

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Sparse recovery space–time adaptive processing (STAP) has an off-grid feature and high computational complexity. To address these shortcomings, this study proposes a self-calibrating STAP algorithm based on sparse Bayesian learning (SBL). The proposed algorithm constructs a dimensionality reduction dictionary by selecting the steering vectors corresponding to atoms with high power values. Then, a small-scale auxiliary dictionary is constructed with a stepwise search approach to calibrate the uniformly discretized dictionary. In this way, the atoms of the auxiliary dictionary can converge to th

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Shekaramiz, Mohammad, and Todd K. Moon. "Compressive Sensing via Variational Bayesian Inference under Two Widely Used Priors: Modeling, Comparison and Discussion." Entropy 25, no. 3 (2023): 511. http://dx.doi.org/10.3390/e25030511.

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Compressive sensing is a sub-Nyquist sampling technique for efficient signal acquisition and reconstruction of sparse or compressible signals. In order to account for the sparsity of the underlying signal of interest, it is common to use sparsifying priors such as Bernoulli–Gaussian-inverse Gamma (BGiG) and Gaussian-inverse Gamma (GiG) priors on the components of the signal. With the introduction of variational Bayesian inference, the sparse Bayesian learning (SBL) methods for solving the inverse problem of compressive sensing have received significant interest as the SBL methods become more e

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Jian, Linlu, Xianpeng Wang, Jinmei Shi, and Xiang Lan. "Robust Sparse Bayesian Learning Scheme for DOA Estimation with Non-Circular Sources." Mathematics 10, no. 6 (2022): 923. http://dx.doi.org/10.3390/math10060923.

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In this paper, a robust DOA estimation scheme based on sparse Bayesian learning (SBL) for non-circular signals in impulse noise and mutual coupling (MC) is proposed. Firstly, the Toeplitz property of the MC matrix is used to eliminate the effect of array MC, and the array aperture is extended by using the properties of the non-circular signal. To eliminate the effect of impulse noise, the outlier part of the impulse noise is reconstructed together with the original signal in the signal matrix, and the DOA coarse estimation is obtained by balancing the accuracy and efficiency of parameter estim

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Bai, Hua, Marco F. Duarte, and Ramakrishna Janaswamy. "Cramér–Rao Bounds for DoA Estimation of Sparse Bayesian Learning with the Laplace Prior." Sensors 23, no. 1 (2022): 307. http://dx.doi.org/10.3390/s23010307.

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In this paper, we derive the Cramér–Rao lower bounds (CRLB) for direction of arrival (DoA) estimation by using sparse Bayesian learning (SBL) and the Laplace prior. CRLB is a lower bound on the variance of the estimator, the change of CRLB can indicate the effect of the specific factor to the DoA estimator, and in this paper a Laplace prior and the three-stage framework are used for the DoA estimation. We derive the CRLBs under different scenarios: (i) if the unknown parameters consist of deterministic and random variables, a hybrid CRLB is derived; (ii) if all the unknown parameters are rando

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Chen, Simiao, Shenyuan Gu, Yilin Zhao, Xuelei Feng, and Yong Shen. "A Comparative Study on Room Impulse Response Reconstruction Using Pattern-Coupled Sparse Bayesian Learning with Different Coupling Structures." Applied Sciences 15, no. 4 (2025): 1859. https://doi.org/10.3390/app15041859.

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Sparse Bayesian learning (SBL) is widely used for sound field reconstruction (SFR). Among various SBL approaches, pattern-coupled SBL has been demonstrated to achieve superior performance. Building on the pattern-coupled SBL framework, this study replaces matrix multiplication with tensor and matrix cross-correlation operations, significantly reducing the algorithm’s spatial and temporal complexity. Furthermore, we compare the performance of different coupling structures within the pattern-coupled SBL method for reconstructing room impulse responses (RIRs) in the time domain. Specifically, we

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Crespo-Cadenas, Carlos, María José Madero-Ayora, Juan A. Becerra, Elías Marqués-Valderrama, and Sergio Cruces. "A Sparse Bayesian Technique to Learn the Frequency-Domain Active Regressors in OFDM Wireless Systems." Sensors 25, no. 14 (2025): 4266. https://doi.org/10.3390/s25144266.

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Digital predistortion and nonlinear behavioral modeling of power amplifiers (PA) have been the subject of intensive research in the time domain (TD), in contrast with the limited number of works conducted in the frequency domain (FD). However, the adoption of orthogonal frequency division multiplexing (OFDM) as a prevalent modulation scheme in current wireless communication standards provides a promising avenue for employing an FD approach. In this work, a procedure to model nonlinear distortion in wireless OFDM systems in the frequency domain is demonstrated for general model structures based

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Wang, Guo, and Wang. "Exploring the Laplace Prior in Radio Tomographic Imaging with Sparse Bayesian Learning towards the Robustness to Multipath Fading." Sensors 19, no. 23 (2019): 5126. http://dx.doi.org/10.3390/s19235126.

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Radio tomographic imaging (RTI) is a technology for target localization by using radiofrequency (RF) sensors in a wireless network. The change of the attenuation field caused by thetarget is represented by a shadowing image, which is then used to estimate the target’s position.The shadowing image can be reconstructed from the variation of the received signal strength (RSS)in the wireless network. However, due to the interference from multi-path fading, not all the RSSvariations are reliable. If the unreliable RSS variations are used for image reconstruction, someartifacts will appear in the sh

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Shekaramiz, Mohammad, Todd Moon, and Jacob Gunther. "Bayesian Compressive Sensing of Sparse Signals with Unknown Clustering Patterns." Entropy 21, no. 3 (2019): 247. http://dx.doi.org/10.3390/e21030247.

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We consider the sparse recovery problem of signals with an unknown clustering pattern in the context of multiple measurement vectors (MMVs) using the compressive sensing (CS) technique. For many MMVs in practice, the solution matrix exhibits some sort of clustered sparsity pattern, or clumpy behavior, along each column, as well as joint sparsity across the columns. In this paper, we propose a new sparse Bayesian learning (SBL) method that incorporates a total variation-like prior as a measure of the overall clustering pattern in the solution. We further incorporate a parameter in this prior to

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Wang, Meiyue, and Shizhong Xu. "A coordinate descent approach for sparse Bayesian learning in high dimensional QTL mapping and genome-wide association studies." Bioinformatics 35, no. 21 (2019): 4327–35. http://dx.doi.org/10.1093/bioinformatics/btz244.

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AbstractMotivationGenomic scanning approaches that detect one locus at a time are subject to many problems in genome-wide association studies and quantitative trait locus mapping. The problems include large matrix inversion, over-conservativeness for tests after Bonferroni correction and difficulty in evaluation of the total genetic contribution to a trait’s variance. Targeting these problems, we take a further step and investigate a multiple locus model that detects all markers simultaneously in a single model.ResultsWe developed a sparse Bayesian learning (SBL) method for quantitative trait

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K., Raghu, and Prameela Kumari N. "Bayesian learning scheme for sparse DOA estimation based on maximum-a-posteriori of hyperparameters." International Journal of Electrical and Computer Engineering (IJECE) 11, no. 4 (2021): 3049. http://dx.doi.org/10.11591/ijece.v11i4.pp3049-3058.

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In this paper, the problem of direction of arrival estimation is addressed by employing Bayesian learning technique in sparse domain. This paper deals with the inference of sparse Bayesian learning (SBL) for both single measurement vector (SMV) and multiple measurement vector (MMV) and its applicability to estimate the arriving signal’s direction at the receiving antenna array; particularly considered to be a uniform linear array. We also derive the hyperparameter updating equations by maximizing the posterior of hyperparameters and exhibit the results for nonzero hyperprior scalars. The resul

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Zheng, Yuanbing, Jizhe Wang, Jian Wang, et al. "GAMP-Based Low-Complexity Sparse Bayesian Learning Channel Estimation for OTFS Systems in V2X Scenarios." Electronics 12, no. 23 (2023): 4722. http://dx.doi.org/10.3390/electronics12234722.

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Vehicle to everything (V2X) is widely regarded as a critical application for future wireless communication networks. In V2X, large relative speeds between vehicles may severely deteriorate the performance of communication between vehicles. Orthogonal time frequency space (OTFS) modulation, which converts time- and frequency-selective channels into non-selective channels in the delay-Doppler (DD) domain, provides a solution for establishing reliable wireless communications in V2X scenarios. However, in the complex multi-scattering scenarios, the channel also suffers from a serious inter-Doppler

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Qi, AiLing, and JiaSen Wang. "Welding defect signal extraction technology based on GAMP-SBL algorithm." Journal of Physics: Conference Series 2221, no. 1 (2022): 012042. http://dx.doi.org/10.1088/1742-6596/2221/1/012042.

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Abstract Flip chip technology has been rapidly developed and widely used in the field of microelectronic packaging, and defect detection has also received more and more attention. Aiming at the problem that noise affects the location and extraction of signal defects in ultrasonic testing, the sparse Bayesian learning based on generalized approximate message passing (GAMP-SBL) algorithm is used to extract signal defects, and the over-complete Gabor dictionary is used to reconstruct signal defects to effectively improve Sparse decomposition algorithm. The precision experiment tested the defect s

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Raghu, K., and Kumari N. Prameela. "Bayesian learning scheme for sparse DOA estimation based on maximum-a-posteriori of hyperparameters." International Journal of Electrical and Computer Engineering (IJECE) 11, no. 4 (2021): 3049–58. https://doi.org/10.11591/ijece.v11i4.pp3049-3058.

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Abstract:

In this paper, the problem of direction of arrival estimation is addressed by employing Bayesian learning technique in sparse domain. This paper deals with the inference of sparse Bayesian learning (SBL) for both single measurement vector (SMV) and multiple measurement vector (MMV) and its applicability to estimate the arriving signal’s direction at the receiving antenna array; particularly considered to be a uniform linear array. We also derive the hyperparameter updating equations by maximizing the posterior of hyperparameters and exhibit the results for nonzero hyperprior scalars. The

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Fang, Tao, Zhi Xia, Songzuo Liu, Xiongbiao Wu, and Lanyue Zhang. "Blind Modulation Identification of Underwater Acoustic MPSK Using Sparse Bayesian Learning and Expectation Maximization." Applied Sciences 10, no. 17 (2020): 5919. http://dx.doi.org/10.3390/app10175919.

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This paper presents a likelihood-based algorithm for identifying different phase shift keying (PSK) modulations, i.e., BPSK, QPSK, and 8PSK. This algorithm selects the modulation type that maximizes a loglikelihood function that is based on the known original constellation associated with the constellation of the received signals for the candidate modulation types. However, there are two problems in non-cooperative underwater acoustic Multiple Phase Shift Keying (MPSK) modulation identification based on the likelihood method. One is the original constellation, which as prior information is unk

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Wang, Ju, Bingqi Shan, Song Duan, and Qin Zhang. "A Highly Efficient Compressive Sensing Algorithm Based on Root-Sparse Bayesian Learning for RFPA Radar." Remote Sensing 16, no. 19 (2024): 3564. http://dx.doi.org/10.3390/rs16193564.

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Off-grid issues and high computational complexity are two major challenges faced by sparse Bayesian learning (SBL)-based compressive sensing (CS) algorithms used for random frequency pulse interval agile (RFPA) radar. Therefore, this paper proposes an off-grid CS algorithm for RFPA radar based on Root-SBL to address these issues. To effectively cope with off-grid issues, this paper derives a root-solving formula inspired by the Root-SBL algorithm for velocity parameters applicable to RFPA radar, thus enabling the proposed algorithm to directly solve the velocity parameters of targets during th

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Zhang, Xuedong, Qi He, and Zaixiao Gong. "A mode separation method based on sparse Bayesian learning for explosive sources in a shallow water waveguide." Journal of the Acoustical Society of America 155, no. 3_Supplement (2024): A46. http://dx.doi.org/10.1121/10.0026749.

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This paper presents a mode separation method based on sparse Bayesian learning (SBL) for explosive sources in a shallow water waveguide. In previous work [Niu et al., JASA, 2021, 4366], the SBL dictionary was constructed by assuming a large number of horizontal wavenumbers and utilized an approximate mode-frequency dispersion relation for low frequencies. Then, modes were separated in the frequency domain by estimating the coefficients of the dictionary atoms. However, challenges inherent to explosive sources, such as bandwidth expansion and the bubble-pulse effect, result in a mismatch in the

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Zhang, Yahao, Ningning Liang, Yixin Yang, and Yunchuan Yang. "Fast Sparse Bayesian Learning Based on Beamformer Power Outputs to Solve Wideband DOA Estimation in Underwater Strong Interference Environment." Electronics 13, no. 8 (2024): 1456. http://dx.doi.org/10.3390/electronics13081456.

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Wideband direction-of-arrival (DOA) estimation is an important task for passive sonar signal processing. Nowadays, sparse Bayesian learning (SBL) attracts much attention due to its good performance. However, performance degrades in the existence of strong interference. This problem can be solved by combining the beamformer and the SBL. The beamformer is a useful tool to suppress interference. Then, the SBL can easily estimate the DOA of the targets from the beamformer power outputs (BPO). Unfortunately, the latter step needs to compute the matrix inversion frequently, which brings some computa

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Zhang, Xinying, Tong Wang, and Degen Wang. "Fast Variational Bayesian Inference for Space-Time Adaptive Processing." Remote Sensing 15, no. 17 (2023): 4334. http://dx.doi.org/10.3390/rs15174334.

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Space-time adaptive processing (STAP) approaches based on sparse Bayesian learning (SBL) have attracted much attention for the benefit of reducing the training samples requirement and accurately recovering sparse signals. However, it has the problem of a heavy computational burden and slow convergence speed. To improve the convergence speed, the variational Bayesian inference (VBI) is introduced to STAP in this paper. Moreover, to improve computing efficiency, a fast iterative algorithm is derived. By constructing a new atoms selection rule, the dimension of the matrix inverse problem can be s

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Dai, Fengzhou, Yuhang Li, Yuanyuan Wang, and Hao Chen. "Efficient Implementation for SBL-Based Coherent Distributed mmWave Radar Imaging." Remote Sensing 15, no. 4 (2023): 1054. http://dx.doi.org/10.3390/rs15041054.

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In a distributed frequency-modulated continuous waveform (FMCW) radar system, the echo data collected are not continuous in the azimuth direction, so the imaging effect of the traditional range-Doppler (RD) algorithm is poor. Sparse Bayesian learning (SBL) is an optimization algorithm based on Bayesian theory that has been successfully applied to high-resolution radar imaging because of its strong robustness and high accuracy. However, SBL is highly computationally complex. Fortunately, with FMCW radar echo data, most of the time-consuming SBL operations involve a Toeplitz-block Toeplitz (TBT)

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Chen , Peng, Zhimin Chen, Xuan Zhang, and Linxi Liu. "SBL-Based Direction Finding Method with Imperfect Array." Electronics 7, no. 12 (2018): 426. http://dx.doi.org/10.3390/electronics7120426.

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The imperfect array degrades the direction finding performance. In this paper, we investigate the direction finding problem in uniform linear array (ULA) system with unknown mutual coupling effect between antennas. By exploiting the target sparsity in the spatial domain, the sparse Bayesian learning (SBL)-based model is proposed and converts the direction finding problem into a sparse reconstruction problem. In the sparse-based model, the off-grid errors are introduced by discretizing the direction area into grids. Therefore, an off-grid SBL model with mutual coupling vector is proposed to ove

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Liu, Cheng, Tong Wang, Shuguang Zhang, and Bing Ren. "A Fast Space-Time Adaptive Processing Algorithm Based on Sparse Bayesian Learning for Airborne Radar." Sensors 22, no. 7 (2022): 2664. http://dx.doi.org/10.3390/s22072664.

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Space-time adaptive processing (STAP) plays an essential role in clutter suppression and moving target detection in airborne radar systems. The main difficulty is that independent and identically distributed (i.i.d) training samples may not be sufficient to guarantee the performance in the heterogeneous clutter environment. Currently, most sparse recovery/representation (SR) techniques to reduce the requirement of training samples still suffer from high computational complexities. To remedy this problem, a fast group sparse Bayesian learning approach is proposed. Instead of employing all the d

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Dong, Xudong, Jun Zhao, Meng Sun, and Xiaofei Zhang. "Non-Circular Signal DOA Estimation with Nested Array via Off-Grid Sparse Bayesian Learning." Sensors 23, no. 21 (2023): 8907. http://dx.doi.org/10.3390/s23218907.

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For the traditional uniform linear array (ULA) direction of arrival (DOA) estimation method with a limited array aperture, a non-circular signal off-grid sparse Bayesian DOA estimation method based on nested arrays is proposed. Firstly, the extended matrix of the received data is constructed by taking advantage of the fact that the statistical properties of non-circular signals are not rotationally invariant. Secondly, we use the difference and sum co-arrays for the nested array technique, thus increasing the array aperture and improving the estimation accuracy. Finally, we take the noise as p

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Wang, Yan, Lei Zhao, Longhao Qiu, Jinjin Wang, and Chenmu Li. "A Sparse Bayesian Learning Method for Direction of Arrival Estimation in Underwater Maneuvering Platform Noise." Journal of Marine Science and Engineering 11, no. 10 (2023): 1879. http://dx.doi.org/10.3390/jmse11101879.

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The underwater maneuvering platform generates self-noise when sailing, which shows spatial directionality to the arrays fixed on the platform. In this paper, it is called spatially colored noise (SCN). The direction of arrival (DOA) estimation results are often influenced by this self-noise, leading to a decrease in estimation accuracy and to the appearance of spurious peaks. To resolve this problem, a sparse Bayesian learning (SBL) method adapted to underwater maneuvering platform noise is proposed in this paper. The SBL framework with unknown SCN is established first. Then, the SCN covarianc

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