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

Author: Grafiati

Published: 24 June 2021

Last updated: 17 February 2022

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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 (October 18, 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 investigate the potential of SBL-EM in a seismic spectral sparse reflectivity inversion, we evaluate the inversion results by comparing them with those of a SBL-SA-based approach in multiple aspects, including the sensitivity to different frequency bands, the robustness to data noise, the lateral continuity of the final profiles and so on. Furthermore, we apply the mean square error (MSE), residual variance (RV) of seismograms and residual energy (RE) between the frequency spectra of the true and inverted reflectivity model to highlight the advantages of the proposed method over the SBL-SA-based approach in terms of spectral sparse reflectivity inversion within a sparse Bayesian learning framework. Multiple examples, including both numerical and field experiments, are carried out to validate the proposed method.

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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 (August 30, 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, these algorithms have limitations of low estimation resolution by insufficient signal length (FFT), required knowledge of the signal frequency component number, and performance degradation at low signal to noise ratio (ESPRIT and RMUSIC). The SBL, which reconstructs a sparse solution from the linear system using the Bayesian framework, has an advantage in frequency detection owing to high resolution from the solution sparsity. Furthermore, in order to improve the robustness of the SBL-based frequency analysis, we exploit multiple measurements over time and space domains that share common frequency components. We compare the estimation results from FFT, ESPRIT, RMUSIC, and SBL using synthetic data, which displays the superior performance of the SBL that has lower estimation errors with a higher recovery ratio. We also apply the SBL to the in-situ data with other schemes and the frequency components from the SBL are revealed as the most effective. In particular, the SBL estimation is remarkably enhanced by the multiple measurements from both space and time domains owing to remaining consistent signal frequency components while diminishing random noise frequency components.

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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 (July 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 for clinical studies.

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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 (July 19, 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 addition, namely EEMD-SBL-ADD, for forecasting crude oil prices, following the “decomposition and ensemble” framework that is widely used in time series analysis. Specifically, EEMD is first used to decompose the raw crude oil price data into components, including several intrinsic mode functions (IMFs) and one residue. Then, we apply SBL to build an individual forecasting model for each component. Finally, the individual forecasting results are aggregated as the final forecasting price by simple addition. To validate the performance of the proposed EEMD-SBL-ADD, we use the publicly-available West Texas Intermediate (WTI) and Brent crude oil spot prices as experimental data. The experimental results demonstrate that the EEMD-SBL-ADD outperforms some state-of-the-art forecasting methodologies in terms of several evaluation criteria such as the mean absolute percent error (MAPE), the root mean squared error (RMSE), the directional statistic (Dstat), the Diebold–Mariano (DM) test, the model confidence set (MCS) test and running time, indicating that the proposed EEMD-SBL-ADD is promising for forecasting crude oil prices.

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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 (June 29, 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 the received data and contributes to immediately extract rough DOA estimates with the root SBL algorithm, which, subsequently, are utilized to obtain the paired rough range estimates. Furthermore, a discrete grid is constructed by the rough DOA and range estimates, and the 2D-SBL model is proposed to optimize the rough DOA and range estimates. Moreover, the expectation-maximization (EM) algorithm is utilized to update the grid points iteratively to further eliminate the errors caused by the off-grid model. Finally, theoretical analyses and numerical simulations illustrate the effectiveness and superiority of the proposed method.

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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, the application of SBL was improved by using modified-Gaussian kernel function and parameters optimization through Particle Swarm Optimization (PSO). To validate the proposed approach, two real-world datasets were used for construction and testing. For deterministic evaluation, the simulation results showed that the proposed model achieves a greater improvement in forecasting accuracy compared with other wind power forecast models. For probabilistic evaluation, the results of indicators also demonstrate that the proposed model has an outstanding performance.

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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 (February 5, 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 (SBL) where the noise is estimated across both sensors and snapshots.

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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 (April 9, 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 locus mapping and genome-wide association studies. This new method adopts a coordinate descent algorithm to estimate parameters (marker effects) by updating one parameter at a time conditional on current values of all other parameters. It uses an L2 type of penalty that allows the method to handle extremely large sample sizes (>100 000). Simulation studies show that SBL often has higher statistical powers and the simulated true loci are often detected with extremely small P-values, indicating that SBL is insensitive to stringent thresholds in significance testing.Availability and implementationAn R package (sbl) is available on the comprehensive R archive network (CRAN) and https://github.com/MeiyueComputBio/sbl/tree/master/R%20packge.Supplementary informationSupplementary data are available at Bioinformatics online.

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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 (March 5, 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 account for the emphasis on the amount of clumpiness in the supports of the solution to improve the recovery performance of sparse signals with an unknown clustering pattern. This parameter does not exist in the other existing algorithms and is learned via our hierarchical SBL algorithm. While the proposed algorithm is constructed for the MMVs, it can also be applied to the single measurement vector (SMV) problems. Simulation results show the effectiveness of our algorithm compared to other algorithms for both SMV and MMVs.

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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 (November 22, 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 shadowing image, which may cause the target’s position being wronglyestimated. Due to the sparse property of the shadowing image, sparse Bayesian learning (SBL) canbe employed for signal reconstruction. Aiming at enhancing the robustness to multipath fading,this paper explores the Laplace prior to characterize the shadowing image under the frameworkof SBL. Bayesian modeling, Bayesian inference and the fast algorithm are presented to achieve themaximum-a-posterior (MAP) solution. Finally, imaging, localization and tracking experiments fromthree different scenarios are conducted to validate the robustness to multipath fading. Meanwhile,the improved computational efficiency of using Laplace prior is validated in the localization-timeexperiment as well.

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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 (August 1, 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 results presented in this paper, shows that the resolution and speed of the proposed algorithm is comparatively improved with almost zero failure rate and minimum mean square error of signal’s direction estimate.

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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 (August 26, 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 unknown. The other is the underwater acoustic multipath channel makes the constellation distort seriously. In this paper, we solved these problems by combining sparse bayesian learning (SBL) with expectation maximization (EM). The specific steps are as follows. Firstly, blind channel equalization can be achieved by channel impulse response (CIR), which is estimated by sparse bayesian learning in single input multi output (SIMO) underwater acoustic channel. Subsequently, we used expectation maximization to compensate amplitude attenuation and phase offset, as the original constellation of MPSK is unknown. Finally, modulation can be successfully identified by the Quasi Hybrid Likelihood Ratio Test (QHLRT). The simulation results show that the channel estimation method based on SBL can eliminate the influence of channel effectively, and the EM algorithm can make the received constellation converge to the preset constellation in the case of unknown original transmit constellation, which effectively solves these two problems. We use the proposed SBL-EM-QHLRT method to achieve an identification rate of more than 95% in underwater acoustic multipath channels with Signal to Noise Ratio (SNR) higher than 15 dB, which provides a new idea for modulation identification of non-cooperative underwater acoustic MPSK.

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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 (December 11, 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 overcome both the mutual coupling and the off-grid effect. With the distribution assumptions of unknown parameters including the noise variance, the off-grid vector, the received signals and the mutual coupling vector, a novel direction finding method based on SBL with unknown mutual coupling effect named DFSMC is proposed, where an expectation-maximum (EM)-based step is adopted by deriving the estimation expressions for all the unknown parameters theoretically. Simulation results show that the proposed DFSMC method can outperform state-of-the-art direction finding methods significantly in the array system with unknown mutual coupling effect.

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Hou, Shuai, Yafeng Wang, and Chao Li. "Uplink Sparse Channel Estimation for Hybrid Millimeter Wave Massive MIMO Systems by UTAMP-SBL." Sensors 21, no. 14 (July 12, 2021): 4760. http://dx.doi.org/10.3390/s21144760.

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The compressive sensing (CS)-based sparse channel estimator is recognized as the most effective solution to the excessive pilot overhead in massive MIMO systems. However, due to the complex signal processing in the wireless communication systems, the measurement matrix in the CS-based channel estimation is sometimes “unfriendly” to the channel recovery. To overcome this problem, in this paper, the state-of-the-art sparse Bayesian learning using approximate message passing with unitary transformation (UTAMP-SBL), which is robust to various measurement matrices, is leveraged to address the multi-user uplink channel estimation for hybrid architecture millimeter wave massive MIMO systems. Specifically, the sparsity of channels in the angular domain is exploited to reduce the pilot overhead. Simulation results demonstrate that the UTAMP-SBL is able to achieve effective performance improvement than other competitors with low pilot overhead.

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Wu, Jiang, Yu Chen, Tengfei Zhou, and Taiyong Li. "An Adaptive Hybrid Learning Paradigm Integrating CEEMD, ARIMA and SBL for Crude Oil Price Forecasting." Energies 12, no. 7 (April 1, 2019): 1239. http://dx.doi.org/10.3390/en12071239.

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Crude oil is one of the main energy sources and its prices have gained increasing attention due to its important role in the world economy. Accurate prediction of crude oil prices is an important issue not only for ordinary investors, but also for the whole society. To achieve the accurate prediction of nonstationary and nonlinear crude oil price time series, an adaptive hybrid ensemble learning paradigm integrating complementary ensemble empirical mode decomposition (CEEMD), autoregressive integrated moving average (ARIMA) and sparse Bayesian learning (SBL), namely CEEMD-ARIMA&SBL-SBL (CEEMD-A&S-SBL), is developed in this study. Firstly, the decomposition method CEEMD, which can reduce the end effects and mode mixing, was employed to decompose the original crude oil price time series into intrinsic mode functions (IMFs) and one residue. Then, ARIMA and SBL with combined kernels were applied to predict target values for the residue and each single IMF independently. Finally, the predicted values of the above two models for each component were adaptively selected based on the training precision, and then aggregated as the final forecasting results using SBL without kernel-tricks. Experiments were conducted on the crude oil spot prices of the West Texas Intermediate (WTI) and Brent crude oil to evaluate the performance of the proposed CEEMD-A&S-SBL. The experimental results demonstrated that, compared with some state-of-the-art prediction models, CEEMD-A&S-SBL can significantly improve the prediction accuracy of crude oil prices in terms of the root mean squared error (RMSE), the mean absolute percent error (MAPE), and the directional statistic (Dstat).

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Liang, Guolong, Zhibo Shi, Longhao Qiu, Sibo Sun, and Tian Lan. "Sparse Bayesian Learning Based Direction-of-Arrival Estimation under Spatially Colored Noise Using Acoustic Hydrophone Arrays." Journal of Marine Science and Engineering 9, no. 2 (January 27, 2021): 127. http://dx.doi.org/10.3390/jmse9020127.

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Direction-of-arrival (DOA) estimation in a spatially isotropic white noise background has been widely researched for decades. However, in practice, such as underwater acoustic ambient noise in shallow water, the ambient noise can be spatially colored, which may severely degrade the performance of DOA estimation. To solve this problem, this paper proposes a DOA estimation method based on sparse Bayesian learning with the modified noise model using acoustic vector hydrophone arrays. Firstly, an applicable linear noise model is established by using the prolate spheroidal wave functions (PSWFs) to characterize spatially colored noise and exploiting the excellent performance of the PSWFs in extrapolating band-limited signals to the space domain. Then, using the proposed noise model, an iterative method for sparse spectrum reconstruction is developed under a sparse Bayesian learning (SBL) framework to fit the actual noise field received by the acoustic vector hydrophone array. Finally, a DOA estimation algorithm under the modified noise model is also presented, which has a superior performance under spatially colored noise. Numerical results validate the effectiveness of the proposed method.

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Ling, Yun, Huotao Gao, Sang Zhou, Lijuan Yang, and Fangyu Ren. "Robust Sparse Bayesian Learning-Based Off-Grid DOA Estimation Method for Vehicle Localization." Sensors 20, no. 1 (January 5, 2020): 302. http://dx.doi.org/10.3390/s20010302.

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With the rapid development of the Internet of Things (IoT), autonomous vehicles have been receiving more and more attention because they own many advantages compared with traditional vehicles. A robust and accurate vehicle localization system is critical to the safety and the efficiency of autonomous vehicles. The global positioning system (GPS) has been widely applied to the vehicle localization systems. However, the accuracy and the reliability of GPS have suffered in some scenarios. In this paper, we present a robust and accurate vehicle localization system consisting of a bistatic passive radar, in which the performance of localization is solely dependent on the accuracy of the proposed off-grid direction of arrival (DOA) estimation algorithm. Under the framework of sparse Bayesian learning (SBL), the source powers and the noise variance are estimated by a fast evidence maximization method, and the off-grid gap is effectively handled by an advanced grid refining strategy. Simulation results show that the proposed method exhibits better performance than the existing sparse signal representation-based algorithms, and performs well in the vehicle localization system.

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Liu, Song, Lan Tang, Yechao Bai, and Xinggan Zhang. "A Sparse Bayesian Learning-Based DOA Estimation Method With the Kalman Filter in MIMO Radar." Electronics 9, no. 2 (February 18, 2020): 347. http://dx.doi.org/10.3390/electronics9020347.

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The direction of arrival (DOA) estimation problem as an essential problem in the radar system is important in radar applications. In this paper, considering a multiple-input and multiple-out (MIMO) radar system, the DOA estimation problem is investigated in the scenario with fast-moving targets. The system model is first formulated, and then by exploiting both the target sparsity in the spatial domain and the temporal correlation of the moving targets, a sparse Bayesian learning (SBL)-based DOA estimation method combined with the Kalman filter (KF) is proposed. Moreover, the performances of traditional sparse-based methods are limited by the off-grid issue, and Taylor-expansion off-grid methods also have high computational complexity and limited performance. The proposed method breaks through the off-grid limit by transforming the problem in the spatial domain to that in the time domain using the movement feature. Simulation results show that the proposed method outperforms the existing methods in the DOA estimation problem for the fast-moving targets.

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Biao, Wang, and He Cheng. "Underwater Target Direction of Arrival Estimation by Small Acoustic Sensor Array Based on Sparse Bayesian Learning." Polish Maritime Research 24, s2 (August 28, 2017): 95–102. http://dx.doi.org/10.1515/pomr-2017-0070.

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Abstract Assuming independently but identically distributed sources, the traditional DOA (direction of arrival) estimation method of underwater acoustic target normally has poor estimation performance and provides inaccurate estimation results. To solve this problem, a new high-accuracy DOA algorithm based on sparse Bayesian learning algorithm is proposed in terms of temporally correlated source vectors. In novel method, we regarded underwater acoustic source as a first-order auto-regressive process. And then we used the new algorithm of multi-vector SBL to reconstruct the signal spatial spectrum. Then we used the CS-MMV model to estimate the DOA. The experiment results have shown the novel algorithm has a higher spatial resolution and estimation accuracy than other DOA algorithms in the cases of less array element space and less snapshots.

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Ojeda, Alejandro, Kenneth Kreutz-Delgado, and Jyoti Mishra. "Bridging M/EEG Source Imaging and Independent Component Analysis Frameworks Using Biologically Inspired Sparsity Priors." Neural Computation 33, no. 9 (August 19, 2021): 2408–38. http://dx.doi.org/10.1162/neco_a_01415.

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Abstract Electromagnetic source imaging (ESI) and independent component analysis (ICA) are two popular and apparently dissimilar frameworks for M/EEG analysis. This letter shows that the two frameworks can be linked by choosing biologically inspired source sparsity priors. We demonstrate that ESI carried out by the sparse Bayesian learning (SBL) algorithm yields source configurations composed of a few active regions that are also maximally independent from one another. In addition, we extend the standard SBL approach to source imaging in two important directions. First, we augment the generative model of M/EEG to include artifactual sources. Second, we modify SBL to allow for efficient model inversion with sequential data. We refer to this new algorithm as recursive SBL (RSBL), a source estimation filter with potential for online and offline imaging applications. We use simulated data to verify that RSBL can accurately estimate and demix cortical and artifactual sources under different noise conditions. Finally, we show that on real error-related EEG data, RSBL can yield single-trial source estimates in agreement with the experimental literature. Overall, by demonstrating that ESI can produce maximally independent sources while simultaneously localizing them in cortical space, we bridge the gap between the ESI and ICA frameworks for M/EEG analysis.

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Srivastava, Suraj, Amrita Mishra, Anupama Rajoriya, Aditya K. Jagannatham, and Gerd Ascheid. "Quasi-Static and Time-Selective Channel Estimation for Block-Sparse Millimeter Wave Hybrid MIMO Systems: Sparse Bayesian Learning (SBL) Based Approaches." IEEE Transactions on Signal Processing 67, no. 5 (March 1, 2019): 1251–66. http://dx.doi.org/10.1109/tsp.2018.2890058.

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Cheng, Yuan, Daiyin Zhu, and Jindong Zhang. "High Precision Sparse Reconstruction Scheme for Multiple Radar Mainlobe Jammings." Electronics 9, no. 8 (July 30, 2020): 1224. http://dx.doi.org/10.3390/electronics9081224.

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Radar mainlobe jamming has attracted considerable attention in the field of electronic countermeasures. When the direction of arrival (DOA) of jamming is close to that of the target, the conventional antijamming methods are ineffective. Generally, mainlobe antijamming method based on blind source separation (BSS) can deteriorate the target direction estimation. Thus in this paper, a high precision sparse reconstruction scheme for multiple radar mainlobe jammings is proposed that does not suffer from failure or performance degradation inherent in the traditional method. First, the mainlobe jamming signal and desired signal components are extracted by using the joint approximation diagonalization of eigenmatrices (JADE) method. Then, oblique projection with sparse Bayesian learning (OP-SBL) method is employed to reconstruct the target with high precision. The proposed method is capable of suppressing at most three radar mainlobe jammers adaptively and also obtain DOA estimation error less than 0.1°. Simulation and experimental results confirm the effectiveness of the proposed method.

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Nannuru, Santosh, Kay L. Gemba, Peter Gerstoft, William S. Hodgkiss, and Christoph F. Mecklenbräuker. "Sparse Bayesian learning with multiple dictionaries." Signal Processing 159 (June 2019): 159–70. http://dx.doi.org/10.1016/j.sigpro.2019.02.003.

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Gerstoft, Peter, Christoph F. Mecklenbrauker, Angeliki Xenaki, and Santosh Nannuru. "Multisnapshot Sparse Bayesian Learning for DOA." IEEE Signal Processing Letters 23, no. 10 (October 2016): 1469–73. http://dx.doi.org/10.1109/lsp.2016.2598550.

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Worley, Bradley. "Scalable Mean-Field Sparse Bayesian Learning." IEEE Transactions on Signal Processing 67, no. 24 (December 15, 2019): 6314–26. http://dx.doi.org/10.1109/tsp.2019.2954504.

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Wipf, D. P., and B. D. Rao. "Sparse Bayesian Learning for Basis Selection." IEEE Transactions on Signal Processing 52, no. 8 (August 2004): 2153–64. http://dx.doi.org/10.1109/tsp.2004.831016.

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Yuan, Sanyi, and Shangxu Wang. "Spectral sparse Bayesian learning reflectivity inversion." Geophysical Prospecting 61, no. 4 (February 27, 2013): 735–46. http://dx.doi.org/10.1111/1365-2478.12000.

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Fan, XiaoBo, and Xingming Li. "Network Tomography via Sparse Bayesian Learning." IEEE Communications Letters 21, no. 4 (April 2017): 781–84. http://dx.doi.org/10.1109/lcomm.2017.2649494.

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Park, Yongsung, Florian Meyer, and Peter Gerstoft. "Sequential sparse Bayesian learning for beamforming." Journal of the Acoustical Society of America 149, no. 4 (April 2021): A85. http://dx.doi.org/10.1121/10.0004594.

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Nannuru, Santosh, Ali Koochakzadeh, Kay L. Gemba, Piya Pal, and Peter Gerstoft. "Sparse Bayesian learning for beamforming using sparse linear arrays." Journal of the Acoustical Society of America 144, no. 5 (November 2018): 2719–29. http://dx.doi.org/10.1121/1.5066457.

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Wang, Lu, Lifan Zhao, Susanto Rahardja, and Guoan Bi. "Alternative to Extended Block Sparse Bayesian Learning and Its Relation to Pattern-Coupled Sparse Bayesian Learning." IEEE Transactions on Signal Processing 66, no. 10 (May 15, 2018): 2759–71. http://dx.doi.org/10.1109/tsp.2018.2816574.

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Zhu, Xin Feng, Bin Li, and Jian Dong Wang. "L1-Norm Sparse Learning and its Application." Applied Mechanics and Materials 88-89 (August 2011): 379–85. http://dx.doi.org/10.4028/www.scientific.net/amm.88-89.379.

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The need on finding sparse representations has attracted more and more people to research it. Researchers have developed many approaches (such as nonnegative constraint, l1-norm sparsity regularization and sparse Bayesian learning with independent Gaussian prior) for encouraging sparse solutions and established some conditions under which the feasible solutions could be found by those approaches. This paper commbined the L1-norm regularization and bayesian learning, called L1-norm sparse bayesian learning, which was inspired by RVM (relative vector machine). L1-norm sparse bayesian learning has found its applications in many fields such as MCR (multivariate curve resolution) and so on. We proposed a new method called BSMCR (bayesian sparse MCR) to enhance the quality of resolve result.

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Zhang, Shuanghui, Yongxiang Liu, and Xiang Li. "Sparse Aperture InISAR Imaging via Sequential Multiple Sparse Bayesian Learning." Sensors 17, no. 10 (October 10, 2017): 2295. http://dx.doi.org/10.3390/s17102295.

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Hu, Nan, Bing Sun, Jiajun Wang, Jisheng Dai, and Chunqi Chang. "Source localization for sparse array using nonnegative sparse Bayesian learning." Signal Processing 127 (October 2016): 37–43. http://dx.doi.org/10.1016/j.sigpro.2016.02.025.

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35

Khanna, Saurabh, and Chandra R. Murthy. "Decentralized Joint-Sparse Signal Recovery: A Sparse Bayesian Learning Approach." IEEE Transactions on Signal and Information Processing over Networks 3, no. 1 (March 2017): 29–45. http://dx.doi.org/10.1109/tsipn.2016.2612120.

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Khanna, Saurabh, and Chandra R. Murthy. "Communication-Efficient Decentralized Sparse Bayesian Learning of Joint Sparse Signals." IEEE Transactions on Signal and Information Processing over Networks 3, no. 3 (September 2017): 617–30. http://dx.doi.org/10.1109/tsipn.2016.2632041.

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Li, Taiyong, and Zhilin Zhang. "Robust Face Recognition via Block Sparse Bayesian Learning." Mathematical Problems in Engineering 2013 (2013): 1–13. http://dx.doi.org/10.1155/2013/695976.

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Face recognition (FR) is an important task in pattern recognition and computer vision. Sparse representation (SR) has been demonstrated to be a powerful framework for FR. In general, an SR algorithm treats each face in a training dataset as a basis function and tries to find a sparse representation of a test face under these basis functions. The sparse representation coefficients then provide a recognition hint. Early SR algorithms are based on a basic sparse model. Recently, it has been found that algorithms based on a block sparse model can achieve better recognition rates. Based on this model, in this study, we use block sparse Bayesian learning (BSBL) to find a sparse representation of a test face for recognition. BSBL is a recently proposed framework, which has many advantages over existing block-sparse-model-based algorithms. Experimental results on the Extended Yale B, the AR, and the CMU PIE face databases show that using BSBL can achieve better recognition rates and higher robustness than state-of-the-art algorithms in most cases.

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MIN, Rui, Yating HU, Yiming PI, and Zongjie CAO. "SAR Tomography Imaging Using Sparse Bayesian Learning." IEICE Transactions on Communications E95-B, no. 1 (2012): 354–57. http://dx.doi.org/10.1587/transcom.e95.b.354.

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39

Fujimaki, Ryohei, Takehisa Yairi, and Kazuo Machida. "Sparse Bayesian Learning for Nonstationary Data Sources." Transactions of the Japanese Society for Artificial Intelligence 23 (2008): 50–57. http://dx.doi.org/10.1527/tjsai.23.50.

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LI, Taiyong, Huijun WANG, Jiang WU, Zhilin ZHANG, and Changjie TANG. "Sparse Bayesian learning for credit risk evaluation." Journal of Computer Applications 33, no. 11 (November 26, 2013): 3094–96. http://dx.doi.org/10.3724/sp.j.1087.2013.03094.

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41

Chen, Zhao, Ruiyang Zhang, Jingwei Zheng, and Hao Sun. "Sparse Bayesian learning for structural damage identification." Mechanical Systems and Signal Processing 140 (June 2020): 106689. http://dx.doi.org/10.1016/j.ymssp.2020.106689.

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42

Hughes, James Michael, Daniel N. Rockmore, and Yang Wang. "Bayesian Learning of Sparse Multiscale Image Representations." IEEE Transactions on Image Processing 22, no. 12 (December 2013): 4972–83. http://dx.doi.org/10.1109/tip.2013.2280188.

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Yuan, Sanyi, Shangxu Wang, Ming Ma, Yongzhen Ji, and Li Deng. "Sparse Bayesian Learning-Based Time-Variant Deconvolution." IEEE Transactions on Geoscience and Remote Sensing 55, no. 11 (November 2017): 6182–94. http://dx.doi.org/10.1109/tgrs.2017.2722223.

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Williams, O., A. Blake, and R. Cipolla. "Sparse Bayesian learning for efficient visual tracking." IEEE Transactions on Pattern Analysis and Machine Intelligence 27, no. 8 (August 2005): 1292–304. http://dx.doi.org/10.1109/tpami.2005.167.

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Tzikas, D. G., A. C. Likas, and N. P. Galatsanos. "Sparse Bayesian Modeling With Adaptive Kernel Learning." IEEE Transactions on Neural Networks 20, no. 6 (June 2009): 926–37. http://dx.doi.org/10.1109/tnn.2009.2014060.

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Jia, Yuheng, Sam Kwong, Wenhui Wu, Ran Wang, and Wei Gao. "Sparse Bayesian Learning-Based Kernel Poisson Regression." IEEE Transactions on Cybernetics 49, no. 1 (January 2019): 56–68. http://dx.doi.org/10.1109/tcyb.2017.2764099.

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Wang, Wei, Jiqing Han, Tieran Zheng, Guibin Zheng, and Mingguang Shao. "Speaker Recognition via Block Sparse Bayesian Learning." International Journal of Multimedia and Ubiquitous Engineering 10, no. 7 (July 31, 2015): 247–54. http://dx.doi.org/10.14257/ijmue.2015.10.7.26.

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Li, Haoran, Jisheng Dai, Tianhong Pan, Chunqi Chang, and Hing Cheung So. "Sparse Bayesian learning approach for baseline correction." Chemometrics and Intelligent Laboratory Systems 204 (September 2020): 104088. http://dx.doi.org/10.1016/j.chemolab.2020.104088.

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Chan, Zeke S. H., Lesley Collins, and N. Kasabov. "Bayesian learning of sparse gene regulatory networks." Biosystems 87, no. 2-3 (February 2007): 299–306. http://dx.doi.org/10.1016/j.biosystems.2006.09.026.

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50

Su, Wu-ge, Hong-qiang Wang, Bin Deng, Rui-jun Wang, and Yu-liang Qin. "Sparse Bayesian learning in ISAR tomography imaging." Journal of Central South University 22, no. 5 (May 2015): 1790–800. http://dx.doi.org/10.1007/s11771-015-2697-1.

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