Статті в журналах: "Multiple Sparse Bayesian Learning"

1

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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2

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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3

Shin, Myoungin, Wooyoung Hong, Keunhwa Lee, and Youngmin Choo. "Passive Sonar Target Identification Using Multiple-Measurement Sparse Bayesian Learning." Sensors 22, no. 21 (November 4, 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 common frequency component using the MM-SBL based on beamforming. The azimuth for each common frequency component was confirmed in the frequency-azimuth plot, through which we identified the target. In addition, we perform target tracking using the target detection results along time, which are derived from the sum of the signal spectrum at the azimuth angle. The performance of the MM-SBL and the conventional target detection method based on energy detection were compared using in-situ data measured near the Korean peninsula, where MM-SBL displays superior detection performance and high-resolution results.

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4

Sun, Bin, Haowen Chen, Xizhang Wei, and Xiang Li. "Multitarget Direct Localization Using Block Sparse Bayesian Learning in Distributed MIMO Radar." International Journal of Antennas and Propagation 2015 (2015): 1–12. http://dx.doi.org/10.1155/2015/903902.

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The target localization in distributed multiple-input multiple-output (MIMO) radar is a problem of great interest. This problem becomes more complicated for the case of multitarget where the measurement should be associated with the correct target. Sparse representation has been demonstrated to be a powerful framework for direct position determination (DPD) algorithms which avoid the association process. In this paper, we explore a novel sparsity-based DPD method to locate multiple targets using distributed MIMO radar. Since the sparse representation coefficients exhibit block sparsity, we use a block sparse Bayesian learning (BSBL) method to estimate the locations of multitarget, which has many advantages over existing block sparse model based algorithms. Experimental results illustrate that DPD using BSBL can achieve better localization accuracy and higher robustness against block coherence and compressed sensing (CS) than popular algorithms in most cases especially for dense targets case.

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5

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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6

Hu, Xiaowei, Ningning Tong, Xingyu He, and Yuchen Wang. "2D Superresolution ISAR Imaging via Temporally Correlated Multiple Sparse Bayesian Learning." Journal of the Indian Society of Remote Sensing 46, no. 3 (October 12, 2017): 387–93. http://dx.doi.org/10.1007/s12524-017-0709-3.

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7

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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8

Narayanaswamy, Anughna, and Ramesha Muniyappa. "Underdetermined direction of arrival estimation for multiple input and multiple outputs sparse channel based on Bayesian learning framework." Indonesian Journal of Electrical Engineering and Computer Science 31, no. 1 (July 1, 2023): 170. http://dx.doi.org/10.11591/ijeecs.v31.i1.pp170-179.

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Direction of arrival (DOA) estimation for a sparse channel has attracted serious attention recently. Better signal analysis and denoising achieve accuracy in DOA determination. This paper proposes an underdetermined DOA estimation for multiple input and multiple outputs (MIMO) sparse channels. A novel multi-kernel-based non-negative sparse Bayesian learning (MK NNSBL) framework is implemented using the multiplied form of basis vector within the manifold matrix for a defined grid. Meanwhile, virtual antenna locations are reconfigured by exploiting the conventional cuckoo search algorithm (CCSA) for the fine reception of incoming signals on a nonuniform linear array (NULA). The simulated results reveal that the novel approach outperforms in its optimal root mean square error (RMSE) for various signal-to-noise ratio (SNR) limits and the compilation time. The convergence comparative graph indicates the improved performance in the proposed framework over existing algorithms.

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9

Qin, Yanhua, Yumin Liu, and Zhongyuan Yu. "Underdetermined DOA estimation using coprime array via multiple measurement sparse Bayesian learning." Signal, Image and Video Processing 13, no. 7 (April 22, 2019): 1311–18. http://dx.doi.org/10.1007/s11760-019-01480-x.

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10

Ma, Jitong, Jiacheng Zhang, Zhengyan Yang, and Tianshuang Qiu. "Off-Grid DOA Estimation Using Sparse Bayesian Learning for MIMO Radar under Impulsive Noise." Sensors 22, no. 16 (August 20, 2022): 6268. http://dx.doi.org/10.3390/s22166268.

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Анотація:

Direction of arrival (DOA) estimation is an essential and fundamental part of array signal processing, which has been widely used in radio monitoring, autonomous driving of vehicles, intelligent navigation, etc. However, it remains a challenge to accurately estimate DOA for multiple-input multiple-output (MIMO) radar in impulsive noise environments. To address this problem, an off-grid DOA estimation method for monostatic MIMO radar is proposed to deal with non-circular signals under impulsive noise. In the proposed method, firstly, based on the property of non-circular signal and array structure, a virtual array output was built and a real-valued sparse representation for the signal model was constructed. Then, an off-grid sparse Bayesian learning (SBL) framework is proposed and further applied to the virtual array to construct novel off-grid sparse model. Finally, off-grid DOA estimation was realized through the solution of the sparse reconstruction with high accuracy even in impulsive noise. Numerous simulations were performed to compare the algorithm with existing methods. Simulation results verify that the proposed off-grid DOA method enables evident performance improvement in terms of accuracy and robustness compared with other works on impulsive noise.

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11

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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12

Liu, Hanwei, Yongshun Zhang, Yiduo Guo, Qiang Wang, and Yifeng Wu. "A Novel STAP Algorithm for Airborne MIMO Radar Based on Temporally Correlated Multiple Sparse Bayesian Learning." Mathematical Problems in Engineering 2016 (2016): 1–9. http://dx.doi.org/10.1155/2016/3986903.

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In a heterogeneous environment, to efficiently suppress clutter with only one snapshot, a novel STAP algorithm for multiple-input multiple-output (MIMO) radar based on sparse representation, referred to as MIMOSR-STAP in this paper, is presented. By exploiting the waveform diversity of MIMO radar, each snapshot at the tested range cell can be transformed into multisnapshots for the phased array radar, which can estimate the high-resolution space-time spectrum by using multiple measurement vectors (MMV) technique. The proposed approach is effective in estimating the spectrum by utilizing Temporally Correlated Multiple Sparse Bayesian Learning (TMSBL). In the sequel, the clutter covariance matrix (CCM) and the corresponding adaptive weight vector can be efficiently obtained. MIMOSR-STAP enjoys high accuracy and robustness so that it can achieve better performance of output signal-to-clutter-plus-noise ratio (SCNR) and minimum detectable velocity (MDV) than the single measurement vector sparse representation methods in the literature. Thus, MIMOSR-STAP can deal with badly inhomogeneous clutter scenario more effectively, especially suitable for insufficient independent and identically distributed (IID) samples environment.

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13

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 (October 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 ( Niu et al., JASA 2021). It uses the approximate modal dispersion relation, connecting the horizontal wavenumbers (phase velocities) for multiple frequencies, to build the dictionary matrix for SBL. Different from warping transforms based on the group slowness (or group speed) dispersion curves, mode separation using SBL is performed in frequency domain based on the phase speed (or equivalently horizontal wavenumbers) dispersion relation. The simulation results demonstrate that the proposed approach is adapted to the environment where both the reflected and refracted modes coexist, whereas the performance of the time warping transformation degrades significantly in this scenario.

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14

You, Kangyong, Wenbin Guo, Tao Peng, Yueliang Liu, Peiliang Zuo, and Wenbo Wang. "Parametric Sparse Bayesian Dictionary Learning for Multiple Sources Localization With Propagation Parameters Uncertainty." IEEE Transactions on Signal Processing 68 (2020): 4194–209. http://dx.doi.org/10.1109/tsp.2020.3009875.

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15

Jabbarian‐Jahromi, Mohammad, Nafiseh Shahbazi, Mohammad Hossein Kahaei, and Aliazam Abbasfar. "Fast two‐dimensional sparse Bayesian learning with application to pulse Doppler multiple‐input–multiple‐output radars." IET Radar, Sonar & Navigation 10, no. 5 (June 2016): 966–75. http://dx.doi.org/10.1049/iet-rsn.2014.0542.

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16

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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17

Wu, Jianning, Jiajing Wang, and Yun Ling. "DCS-based MBSBL joint reconstruction of multi-sensors data for energy-efficient telemonitoring of human activity." International Journal of Distributed Sensor Networks 14, no. 3 (March 2018): 155014771876761. http://dx.doi.org/10.1177/1550147718767612.

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The joint reconstruction of nonsparse multi-sensors data with high quality is a challenging issue in human activity telemonitoring. In this study, we proposed a novel joint reconstruction algorithm combining distributed compressed sensing with multiple block sparse Bayesian learning. Its basic idea is that based on the joint sparsity model, the distributed compressed sensing technique is first applied to simultaneously compress the multi-sensors data for gaining the high-correlation information regarding activity as well as the energy efficiency of sensors, and then, the multiple block sparse Bayesian learning technique is employed to jointly recover nonsparse multi-sensors data with high fidelity by exploiting the joint block sparsity. The multi-sensors acceleration data from an open wearable action recognition database are selected to assess the practicality of our proposed technique. The sparse representation classification model is used to classify activity patterns using the jointly reconstructed data in order to further examine the effectiveness of our proposed method. The results showed that when compression rates are selected properly, our proposed technique can gain the best joint reconstruction performance as well as energy efficiency of sensors, which greatly contributes to the best sparse representation classification–based activity classification performance. This has a great potential for energy-efficient telemonitoring of human activity.

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18

Wang, Feng-hua, Zhi-chao Sha, Zhang-meng Liu, and Zhi-tao Huang. "A Frequency Tracking Method for Multiple Frequency-hopping Signals Based on Sparse Bayesian Learning." Journal of Electronics & Information Technology 35, no. 6 (February 17, 2014): 1395–99. http://dx.doi.org/10.3724/sp.j.1146.2012.01493.

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19

Hongchao Liu, Bo Jiu, Hongwei Liu, and Zheng Bao. "A Novel ISAR Imaging Algorithm for Micromotion Targets Based on Multiple Sparse Bayesian Learning." IEEE Geoscience and Remote Sensing Letters 11, no. 10 (October 2014): 1772–76. http://dx.doi.org/10.1109/lgrs.2014.2308536.

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20

Dai, Wei, and Huawei Chen. "Multiple Speech Sources Localization in Room Reverberant Environment Using Spherical Harmonic Sparse Bayesian Learning." IEEE Sensors Letters 3, no. 2 (February 2019): 1–4. http://dx.doi.org/10.1109/lsens.2018.2890129.

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21

Zhang, Han, Jiadong Hua, Fei Gao, and Jing Lin. "Efficient Lamb-wave based damage imaging using multiple sparse Bayesian learning in composite laminates." NDT & E International 116 (December 2020): 102277. http://dx.doi.org/10.1016/j.ndteint.2020.102277.

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22

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 (December 28, 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 random, a Bayesian CRLB is derived, and the marginalized Bayesian CRLB is obtained by marginalizing out the nuisance parameter. We also derive the CRLBs of the hyperparameters involved in the three-stage model and explore the effect of multiple snapshots to the CRLBs. We compare the derived CRLBs of SBL, finding that the marginalized Bayesian CRLB is tighter than other CRLBs when SNR is low and the differences between CRLBs become smaller when SNR is high. We also study the relationship between the mean squared error of the source magnitudes and the CRLBs, including numerical simulation results with a variety of antenna configurations such as different numbers of receivers and different noise conditions.

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23

Zhang, Yu, Yu Wang, Jing Jin, and Xingyu Wang. "Sparse Bayesian Learning for Obtaining Sparsity of EEG Frequency Bands Based Feature Vectors in Motor Imagery Classification." International Journal of Neural Systems 27, no. 02 (December 28, 2016): 1650032. http://dx.doi.org/10.1142/s0129065716500325.

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Effective common spatial pattern (CSP) feature extraction for motor imagery (MI) electroencephalogram (EEG) recordings usually depends on the filter band selection to a large extent. Subband optimization has been suggested to enhance classification accuracy of MI. Accordingly, this study introduces a new method that implements sparse Bayesian learning of frequency bands (named SBLFB) from EEG for MI classification. CSP features are extracted on a set of signals that are generated by a filter bank with multiple overlapping subbands from raw EEG data. Sparse Bayesian learning is then exploited to implement selection of significant features with a linear discriminant criterion for classification. The effectiveness of SBLFB is demonstrated on the BCI Competition IV IIb dataset, in comparison with several other competing methods. Experimental results indicate that the SBLFB method is promising for development of an effective classifier to improve MI classification.

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24

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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25

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 (April 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 phase correction factors of different targets, followed by phase compensation applied to the extended aperture manifold vectors for DOA estimation. Simulation and experimental data results demonstrate that this proposed method successfully extends the aperture and provides higher azimuth resolution and accuracy compared to conventional beamforming (CBF) and SBL without extension. Compared with the traditional ETAM methods, the proposed method still performs well even when the array elements are not overlapped during the motion.

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Xu, Zenglin, Shandian Zhe, Yuan Qi, and Peng Yu. "Association Discovery and Diagnosis of Alzheimers Disease with Bayesian Multiview Learning." Journal of Artificial Intelligence Research 56 (June 23, 2016): 247–68. http://dx.doi.org/10.1613/jair.4956.

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The analysis and diagnosis of Alzheimers disease (AD) can be based on genetic variations, e.g., single nucleotide polymorphisms (SNPs) and phenotypic traits, e.g., Magnetic Resonance Imaging (MRI) features. We consider two important and related tasks: i) to select genetic and phenotypical markers for AD diagnosis and ii) to identify associations between genetic and phenotypical data. While previous studies treat these two tasks separately, they are tightly coupled because underlying associations between genetic variations and phenotypical features contain the biological basis for a disease. Here we present a new sparse Bayesian approach for joint association study and disease diagnosis. In this approach, common latent features are extracted from different data sources based on sparse projection matrices and used to predict multiple disease severity levels; in return, the disease status can guide the discovery of relationships between data sources. The sparse projection matrices not only reveal interactions between data sources but also select groups of biomarkers related to the disease. Moreover, to take advantage of the linkage disequilibrium (LD) measuring the non-random association of alleles, we incorporate a graph Laplacian type of prior in the model. To learn the model from data, we develop an efficient variational inference algorithm. Analysis on an imaging genetics dataset for the study of Alzheimers Disease (AD) indicates that our model identifies biologically meaningful associations between genetic variations and MRI features, and achieves significantly higher accuracy for predicting ordinal AD stages than the competing methods.

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27

Li, Ninghui, Xiaokuan Zhang, Binfeng Zong, Fan Lv, Jiahua Xu, and Zhaolong Wang. "Wideband DOA Estimation Utilizing a Hierarchical Prior Based on Variational Bayesian Inference." Electronics 12, no. 14 (July 14, 2023): 3074. http://dx.doi.org/10.3390/electronics12143074.

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Анотація:

The direction-of-arrival (DOA) estimation of wideband signals, based on sparse signal reconstruction, has recently been proposed, owing to its unique high-resolution performance. As a typical tool of sparse signal reconstruction, sparse Bayesian learning (SBL) enhances little sparsity in most works, leading to a non-robust local fitting. To significantly enhance sparsity, we proposed a novel hierarchical Bayesian prior framework, and deduced a novel iterative approach. It was discovered that the iterative approach had a lower computational complexity than the majority of current state-of-the-art algorithms. Besides, the proposed approach achieves a high angular estimation accuracy and sparsity performance, by utilizing the joint sparsity of the multiple measurement vector (MMV) models. Moreover, the approach stabilizes the estimated values between different frequencies or snapshots, so as to obtain a flat spatial spectrum. Extensive simulation results are presented, to demonstrate the superior performance of our method.

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Chen, Shuo, Haojie Li, Lanjie Zhang, Mingyu Zhou, and Xuehua Li. "Block Sparse Bayesian Learning Based Joint User Activity Detection and Channel Estimation in Grant-Free MIMO-NOMA." Drones 7, no. 1 (December 31, 2022): 27. http://dx.doi.org/10.3390/drones7010027.

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In the massive machine type of communication (mMTC), grant-free non-orthogonal multiple access (NOMA) is receiving more and more attention because it can skip the complex grant process to allocate non-orthogonal resources to serve more users. To address the limited wireless resources and substantial connection challenges, combining grant-free NOMA and multiple-input multiple-output (MIMO) is crucial to further improve the system’s capacity. In the grant-free MIMO-NOMA system, the base station should obtain the relevant information of the user before data detection. Thus, user activity detection (UAD) and channel estimation (CE) are two problems that should be solved urgently. In this paper, we fully consider the sparse characteristics of signals and the spatial correlation between multiple antennas in the grant-free MIMO-NOMA system. Then, we propose a spatial correlation block sparse Bayesian learning (SC-BSBL) algorithm to address the joint UAD and CE problems. First, by fully mining the block sparsity of signals in the grant-free MIMO-NOMA system, we model the joint UAD and CE problem as a three-dimensional block sparse signal recovery problem. Second, we derive the cost function based on the hierarchical Bayesian theory and spatial correlation. Finally, to estimate the channel and the set of active users, we optimize the cost function with fast marginal likelihood maximization. The simulation results indicate that, compared with the existing algorithms, SC-BSBL can always fully use the signal sparsity and spatial correlation to accurately complete UAD and CE under various user activation probabilities, SNRs, and the number of antennas. The normalized mean square error of CE can be reduced to 0.01, and the UAD error rate can be less than 10−5.

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29

Yang, Zhao-Xu, Hai-Jun Rong, Pak Kin Wong, Plamen Angelov, Chi Man Vong, Chi Wai Chiu, and Zhi-Xin Yang. "A Novel Multiple Feature-Based Engine Knock Detection System using Sparse Bayesian Extreme Learning Machine." Cognitive Computation 14, no. 2 (January 19, 2022): 828–51. http://dx.doi.org/10.1007/s12559-021-09945-3.

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30

You, Kangyong, Wenbin Guo, Yueliang Liu, Wenbo Wang, and Zhuo Sun. "Grid Evolution: Joint Dictionary Learning and Sparse Bayesian Recovery for Multiple Off-Grid Targets Localization." IEEE Communications Letters 22, no. 10 (October 2018): 2068–71. http://dx.doi.org/10.1109/lcomm.2018.2863374.

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31

Zhang, Jian A., Zhuo Chen, Peng Cheng, and Xiaojing Huang. "Multiple-measurement vector based implementation for single-measurement vector sparse Bayesian learning with reduced complexity." Signal Processing 118 (January 2016): 153–58. http://dx.doi.org/10.1016/j.sigpro.2015.06.020.

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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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Анотація:

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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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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Liu, Yang, Na Dong, Xiaohui Zhang, Xin Zhao, Yinghui Zhang, and Tianshuang Qiu. "DOA Estimation for Massive MIMO Systems with Unknown Mutual Coupling Based on Block Sparse Bayesian Learning." Sensors 22, no. 22 (November 9, 2022): 8634. http://dx.doi.org/10.3390/s22228634.

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Obtaining accurate angle parameters using direction-of-arrival (DOA) estimation algorithms is crucial for acquiring channel state information (CSI) in massive multiple-input multiple-output (MIMO) systems. However, the performance of the existing algorithms deteriorates severely due to mutual coupling between antenna elements in practical engineering. Therefore, for solving the array mutual coupling, the array output signal vector is modeled by mutual coupling coefficients and the DOA estimation problem is transformed into block sparse signal reconstruction and parameter optimization in this paper. Then, a novel sparse Bayesian learning (SBL)-based algorithm is proposed, in which the expectation-maximum (EM) algorithm is used to estimate the unknown parameters iteratively, and the convergence speed of the algorithm is enhanced by utilizing the approximate approximation. Moreover, considering the off-grid error caused by discretization processes, the grid refinement is carried out using the polynomial roots to realize the dynamic update of the grid points, so as to improve the DOA estimation accuracy. Simulation results show that compared with the existing algorithms, the proposed algorithm is more robust to mutual coupling and off-grid error and can obtain better estimation performance.

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35

Wu, Biao, Yong Huang, Xiang Chen, Sridhar Krishnaswamy, and Hui Li. "Guided-wave signal processing by the sparse Bayesian learning approach employing Gabor pulse model." Structural Health Monitoring 16, no. 3 (August 29, 2016): 347–62. http://dx.doi.org/10.1177/1475921716665252.

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Guided waves have been used for structural health monitoring to detect damage or defects in structures. However, guided wave signals often involve multiple modes and noise. Extracting meaningful damage information from the received guided wave signal becomes very challenging, especially when some of the modes overlap. The aim of this study is to develop an effective way to deal with noisy guided-wave signals for damage detection as well as for de-noising. To achieve this goal, a robust sparse Bayesian learning algorithm is adopted. One of the many merits of this technique is its good performance against noise. First, a Gabor dictionary is designed based on the information of the noisy signal. Each atom of this dictionary is a modulated Gaussian pulse. Then the robust sparse Bayesian learning technique is used to efficiently decompose the guided wave signal. After signal decomposition, a two-step matching scheme is proposed to extract meaningful waveforms for damage detection and localization. Results from numerical simulations and experiments on isotropic aluminum plate structures are presented to verify the effectiveness of the proposed approach in mode identification and signal de-noising for damage detection.

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36

Wu, Jingjing, Siwei Li, Saiwen Zhang, Danying Lin, Bin Yu, and Junle Qu. "Fast analysis method for stochastic optical reconstruction microscopy using multiple measurement vector model sparse Bayesian learning." Optics Letters 43, no. 16 (August 10, 2018): 3977. http://dx.doi.org/10.1364/ol.43.003977.

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37

Duan, Keqing, Zetao Wang, Wenchong Xie, Hui Chen, and Yongliang Wang. "Sparsity‐based STAP algorithm with multiple measurement vectors via sparse Bayesian learning strategy for airborne radar." IET Signal Processing 11, no. 5 (July 2017): 544–53. http://dx.doi.org/10.1049/iet-spr.2016.0183.

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38

Wen, Chao, Lu Chen, Pengting Duan, and Xuefeng Cui. "MIMO Radar Imaging With Multiple Probing Pulses for 2D Off-Grid Targets via Variational Sparse Bayesian Learning." IEEE Access 8 (2020): 147591–603. http://dx.doi.org/10.1109/access.2020.3015223.

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39

Joshi, Deepti, Andre St-Hilaire, Taha Ouarda, and Anik Daigle. "Statistical downscaling of precipitation and temperature using sparse Bayesian learning, multiple linear regression and genetic programming frameworks." Canadian Water Resources Journal / Revue canadienne des ressources hydriques 40, no. 4 (October 2, 2015): 392–408. http://dx.doi.org/10.1080/07011784.2015.1089191.

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40

Joshi, Deepti, André St-Hilaire, Anik Daigle, and Taha B. M. J. Ouarda. "Databased comparison of Sparse Bayesian Learning and Multiple Linear Regression for statistical downscaling of low flow indices." Journal of Hydrology 488 (April 2013): 136–49. http://dx.doi.org/10.1016/j.jhydrol.2013.02.040.

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41

Tang, Jian, Jun Yan, Lei Ji, Ming Zhang, Shaodan Guo, Ning Liu, Xianfang Wang, and Zheng Chen. "Collaborative Users’ Brand Preference Mining across Multiple Domains from Implicit Feedbacks." Proceedings of the AAAI Conference on Artificial Intelligence 25, no. 1 (August 4, 2011): 477–82. http://dx.doi.org/10.1609/aaai.v25i1.7899.

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Advanced e-applications require comprehensive knowledge about their users’ preferences in order to provide accurate personalized services. In this paper, we propose to learn users’ preferences to product brands from their implicit feedbacks such as their searching and browsing behaviors in user Web browsing log data. The user brand preference learning problem is challenge since (1) the users’ implicit feedbacks are extremely sparse in various product domains; and (2) we can only observe positive feedbacks from users’ behaviors. In this paper, we propose a latent factor model to collaboratively mine users’ brand preferences across multiple domains simultaneously. By collective learning, the learning processes in all the domains are mutually enhanced and hence the problem of data scarcity in each single domain can be effectively addressed. On the other hand, we learn our model with an adaption of the Bayesian personalized ranking (BPR) optimization criterion which is a general learning framework for collaborative filtering from implicit feedbacks. Experiments with both synthetic and real world datasets show that our proposed model significantly outperforms the baselines.

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Lin, Jiali, and Inyoung Kim. "Bayesian multiple Gaussian graphical models for multilevel variables from unknown classes." Statistical Methods in Medical Research 31, no. 4 (February 15, 2022): 594–611. http://dx.doi.org/10.1177/09622802211022405.

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Gaussian graphical models are a powerful tool for investigating the conditional dependency structure between random variables by estimating sparse precision matrices and can infer networks among variables from multiple classes. Many studies assume that classes of observations are given and use methods to learn the network structures within one level (e.g. pathways or genes). In most cases, however, heterogeneous data may be obtained at different levels. Therefore, in this paper, we consider the learning of multiple connected graphs with multilevel variables from unknown classes. We estimate the classes of the observations from the mixture distributions by evaluating the Bayes factor and learn about the network structures by fitting a neighborhood-selection algorithm. This approach can be used to identify the class memberships and reveal the network structures for lower level and higher level variables simultaneously. Unlike most existing methods, which solve this problem using frequentest approaches, we assess an alternative and novel hierarchical Bayesian approach for incorporating prior knowledge. We demonstrate the unique advantages of our methods through several simulations. A breast cancer application shows that our model’s results can provide insight into biological studies.

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43

Becker, Keith, Jim Sprigg, and Alex Cosmas. "Estimating individual promotional campaign impacts through Bayesian inference." Journal of Consumer Marketing 31, no. 6/7 (November 4, 2014): 541–52. http://dx.doi.org/10.1108/jcm-06-2014-1006.

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Purpose – The purpose of this paper is to estimate individual promotional campaign impacts through Bayesian inference. Conventional statistics have worked well for analyzing the impact of direct marketing promotions on purchase behavior. However, many modern marketing programs must drive multiple purchase objectives, requiring more precise arbitration between multiple offers and collection of more data with which to differentiate individuals. This often results in datasets that are highly dimensional, yet also sparse, straining the power of statistical methods to properly estimate the effect of promotional treatments. Design/methodology/approach – Improvements in computing power have enabled new techniques for predicting individual behavior. This work investigates a probabilistic machine-learned Bayesian approach to predict individual impacts driven by promotional campaign offers for a leading global travel and hospitality chain. Comparisons were made to a linear regression, representative of the current state of practice. Findings – The findings of this work focus on comparing a machine-learned Bayesian approach with linear regression (which is representative of the current state of practice among industry practitioners) in the analysis of a promotional campaign across three key areas: highly dimensional data, sparse data and likelihood matching. Research limitations/implications – Because the findings are based on a single campaign, future work includes generalizing results across multiple promotional campaigns. Also of interest for future work are comparisons of the technique developed here with other techniques from academia. Practical implications – Because the Bayesian approach allows estimation of the influence of the promotion for each hypothetical customer’s set of promotional attributes, even when no exact look-alikes exist in the control group, a number of possible applications exist. These include optimal campaign design (given the ability to estimate the promotional attributes that are likely to drive the greatest incremental spend in a hypothetical deployment) and operationalizing efficient audience selection given the model’s individualized estimates, reducing the risk of marketing overcommunication, which can prompt costly unsubscriptions. Originality/value – The original contribution is the application of machine-learning to Bayesian Belief Network construction in the context of analyzing a multi-channel promotional campaign’s impact on individual customers. This is of value to practitioners seeking alternatives for campaign analysis for applications in which more commonly used models are not well-suited, such as the three key areas that this paper highlights: highly dimensional data, sparse data and likelihood matching.

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44

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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45

Ye, Qing, and Changhua Liu. "Simultaneous Fault Diagnosis Based on Hierarchical Multi-Label Classification and Sparse Bayesian Extreme Learning Machine." Applied Sciences 13, no. 4 (February 13, 2023): 2376. http://dx.doi.org/10.3390/app13042376.

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This paper proposes an intelligent simultaneous fault diagnosis model based on a hierarchical multi-label classification strategy and sparse Bayesian extreme learning machine. The intelligent diagnosis model compares the similarity between an unknown sample to be diagnosed and each single fault mode, then outputs the probability of each fault mode occurring. First, multiple two-class sub-classifiers based on SBELM are trained by using single-fault samples to extract the correlation between various pairs of single-fault, and the sub-classifiers are integrated with the proposed hierarchical multi-label classification (HMLC) strategy to form the diagnostic model based on HMLC-SBELM. Then, samples of single faults and simultaneous faults are used to generate the optimal discriminative thresholds by using optimization algorithms. Finally, the probabilistic output generated by the HMLC-SBELM-based model is transformed into the final fault modes by using the optimal discriminative threshold. The model performance is evaluated by using actual vibration signals of the main reducer and is compared with several classical models. The contrastive results indicate that the proposed model is more accurate, efficient, and stable.

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46

Li, Rui, Ying Luo, Qun Zhang, Yijun Chen, and Jia Liang. "Bistatic Radar Coincidence Imaging Based on Multiple Measurement Vectors for Rotating Cone-Shaped Targets." Journal of Sensors 2020 (August 13, 2020): 1–10. http://dx.doi.org/10.1155/2020/3878525.

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Bistatic radar imaging can overcome limitations of monostatic radar imaging and obtain abundant target feature information; thus, it is followed with interest. Different from bistatic inverse synthetic aperture radar (Bi-ISAR) imaging, bistatic radar coincidence imaging (Bi-RCI) provides a new tack on the bistatic radar imaging technique. In this paper, a Bi-RCI based on multiple measurement vectors (MMV) for rotating cone-shaped targets is proposed to realize Bi-RCI coherent processing and improve imaging performance. Based on the mixed mode signals, a MMV parametric model is established and measurement number coarse selection is proposed. Finally, a modified sparse Bayesian learning (MSBL) algorithm is introduced to reconstruct the target image. Simulation results demonstrate the validity and the superiority of the proposed method.

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47

Zhao, Lv, Yi Dan Su, Hua Qin, and Pian Pian Ma. "Study of Multiple-Kernel Relevance Vector Machine Based on Kernel Alignment." Applied Mechanics and Materials 239-240 (December 2012): 1308–12. http://dx.doi.org/10.4028/www.scientific.net/amm.239-240.1308.

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The relevance vector machine (RVM) was a Bayesian framework for learning sparse regression models and classifiers, it used single kernel function to map training data from low dimension sample space to high dimension feature space. The prediction accuracy and generalization of traditional single-kernel RVM (sRVM) were not ideal both in classification and regression, so we constructed homogeneous and heterogeneous multiple kernels function (MKF) by kernel function combination in which we testified the validity of basic kernel function (BKF) and its parameters we employed by kernel alignment (KA), then we acquired optimized multiple-kernel RVM (mRVM). Experiment results on LIBSVM datasets not only indicate that both homogeneous and heterogeneous multiple-kernel RVM we constructed possess lower error rate in classification and smaller root mean square (RMS) in regression than single-kernel RVM, but also prove the effectiveness of kernel alignment.

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48

Hujsak, Karl, Benjamin D. Myers, Eric Roth, Yue Li, and Vinayak P. Dravid. "Suppressing Electron Exposure Artifacts: An Electron Scanning Paradigm with Bayesian Machine Learning." Microscopy and Microanalysis 22, no. 4 (July 26, 2016): 778–88. http://dx.doi.org/10.1017/s1431927616011417.

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AbstractElectron microscopy of biological, polymeric, and other beam-sensitive structures is often hampered by deleterious electron beam interactions. In fact, imaging of such beam-sensitive materials is limited by the allowable radiation dosage rather that capabilities of the microscope itself, which has been compounded by the availability of high brightness electron sources. Reducing dwell times to overcome dose-related artifacts, such as radiolysis and electrostatic charging, is challenging due to the inherently low contrast in imaging of many such materials. These challenges are particularly exacerbated during dynamic time-resolved, fluidic cell imaging, or three-dimensional tomographic reconstruction—all of which undergo additional dosage. Thus, there is a pressing need for the development of techniques to produce high-quality images at ever lower electron doses. In this contribution, we demonstrate direct dose reduction and suppression of beam-induced artifacts through under-sampling pixels, by as much as 80% reduction in dosage, using a commercial scanning electron microscope with an electrostatic beam blanker and a dictionary learning in-painting algorithm. This allows for multiple sparse recoverable images to be acquired at the cost of one fully sampled image. We believe this approach may open new ways to conduct imaging, which otherwise require compromising beam current and/or exposure conditions.

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49

Qiao, Gang, Qingjun Song, Lu Ma, Zongxin Sun, and Jiarong Zhang. "Channel prediction based temporal multiple sparse bayesian learning for channel estimation in fast time-varying underwater acoustic OFDM communications." Signal Processing 175 (October 2020): 107668. http://dx.doi.org/10.1016/j.sigpro.2020.107668.

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50

Wang, Degen, Tong Wang, Weichen Cui, and Cheng Liu. "Adaptive Support-Driven Sparse Recovery STAP Method with Subspace Penalty." Remote Sensing 14, no. 18 (September 7, 2022): 4463. http://dx.doi.org/10.3390/rs14184463.

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Detecting a moving target is an attractive topic in many fields, such as remote sensing. Space-time adaptive processing (STAP) plays a key role in detecting moving targets in strong clutter backgrounds for airborne early warning radar systems. However, STAP suffers serious clutter suppression performance loss when the number of training samples is insufficient due to the inhomogeneous clutter environment. In this article, an efficient sparse recovery STAP algorithm is proposed. First, inspired by the relationship between multiple sparse Bayesian learning (M-SBL) and subspace-based hybrid greedy algorithms, a new optimization objective function based on a subspace penalty is established. Second, the closed-form solution of each minimization step is obtained through the alternating minimization algorithm, which can guarantee the convergence of the algorithm. Finally, a restart strategy is used to adaptively update the support, which reduces the computational complexity. Simulation results show that the proposed algorithm has excellent performance in clutter suppression, convergence speed and running time with insufficient training samples.

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