期刊文献+
共找到16篇文章
< 1 >
每页显示 20 50 100
Joint Multi-Domain Channel Estimation Based on Sparse Bayesian Learning for OTFS System 被引量:7
1
作者 Yong Liao Xue Li 《China Communications》 SCIE CSCD 2023年第1期14-23,共10页
Since orthogonal time-frequency space(OTFS)can effectively handle the problems caused by Doppler effect in high-mobility environment,it has gradually become a promising candidate for modulation scheme in the next gene... Since orthogonal time-frequency space(OTFS)can effectively handle the problems caused by Doppler effect in high-mobility environment,it has gradually become a promising candidate for modulation scheme in the next generation of mobile communication.However,the inter-Doppler interference(IDI)problem caused by fractional Doppler poses great challenges to channel estimation.To avoid this problem,this paper proposes a joint time and delayDoppler(DD)domain based on sparse Bayesian learning(SBL)channel estimation algorithm.Firstly,we derive the original channel response(OCR)from the time domain channel impulse response(CIR),which can reflect the channel variation during one OTFS symbol.Compare with the traditional channel model,the OCR can avoid the IDI problem.After that,the dimension of OCR is reduced by using the basis expansion model(BEM)and the relationship between the time and DD domain channel model,so that we have turned the underdetermined problem into an overdetermined problem.Finally,in terms of sparsity of channel in delay domain,SBL algorithm is used to estimate the basis coefficients in the BEM without any priori information of channel.The simulation results show the effectiveness and superiority of the proposed channel estimation algorithm. 展开更多
关键词 OTFS sparse bayesian learning basis expansion model channel estimation
下载PDF
Vector Approximate Message Passing with Sparse Bayesian Learning for Gaussian Mixture Prior 被引量:2
2
作者 Chengyao Ruan Zaichen Zhang +3 位作者 Hao Jiang Jian Dang Liang Wu Hongming Zhang 《China Communications》 SCIE CSCD 2023年第5期57-69,共13页
Compressed sensing(CS)aims for seeking appropriate algorithms to recover a sparse vector from noisy linear observations.Currently,various Bayesian-based algorithms such as sparse Bayesian learning(SBL)and approximate ... Compressed sensing(CS)aims for seeking appropriate algorithms to recover a sparse vector from noisy linear observations.Currently,various Bayesian-based algorithms such as sparse Bayesian learning(SBL)and approximate message passing(AMP)based algorithms have been proposed.For SBL,it has accurate performance with robustness while its computational complexity is high due to matrix inversion.For AMP,its performance is guaranteed by the severe restriction of the measurement matrix,which limits its application in solving CS problem.To overcome the drawbacks of the above algorithms,in this paper,we present a low complexity algorithm for the single linear model that incorporates the vector AMP(VAMP)into the SBL structure with expectation maximization(EM).Specifically,we apply the variance auto-tuning into the VAMP to implement the E step in SBL,which decrease the iterations that require to converge compared with VAMP-EM algorithm when using a Gaussian mixture(GM)prior.Simulation results show that the proposed algorithm has better performance with high robustness under various cases of difficult measurement matrices. 展开更多
关键词 sparse bayesian learning approximate message passing compressed sensing expectation propagation
下载PDF
DOA estimation based on multi-frequency joint sparse Bayesian learning for passive radar 被引量:1
3
作者 WEN Jinfang YI Jianxin +2 位作者 WAN Xianrong GONG Ziping SHEN Ji 《Journal of Systems Engineering and Electronics》 SCIE EI CSCD 2022年第5期1052-1063,共12页
This paper considers multi-frequency passive radar and develops a multi-frequency joint direction of arrival(DOA)estimation algorithm to improve estimation accuracy and resolution.The developed algorithm exploits the ... This paper considers multi-frequency passive radar and develops a multi-frequency joint direction of arrival(DOA)estimation algorithm to improve estimation accuracy and resolution.The developed algorithm exploits the sparsity of targets in the spatial domain.Specifically,we first extract the required frequency channel data and acquire the snapshot data through a series of preprocessing such as clutter suppression,coherent integration,beamforming,and constant false alarm rate(CFAR)detection.Then,based on the framework of sparse Bayesian learning,the target’s DOA is estimated by jointly extracting the multi-frequency data via evidence maximization.Simulation results show that the developed algorithm has better estimation accuracy and resolution than other existing multi-frequency DOA estimation algorithms,especially under the scenarios of low signalto-noise ratio(SNR)and small snapshots.Furthermore,the effectiveness is verified by the field experimental data of a multi-frequency FM-based passive radar. 展开更多
关键词 multi-frequency passive radar DOA estimation sparse bayesian learning small snapshot low signal-to-noise ratio(SNR)
下载PDF
EARLY CATARACT DETECTION BY DYNAMIC LIGHT SCATTERING WITH SPARSE BAYESIAN LEARNING
4
作者 SU-LONG NYEO RAFAT R.ANSAR 《Journal of Innovative Optical Health Sciences》 SCIE EI CAS 2009年第3期303-313,共11页
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 reco... 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 ofα-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. 展开更多
关键词 CATARACT dynamic light scattering diagnostic algorithm sparse bayesian learning(SBL).
下载PDF
Online identification of time-varying dynamical systems for industrial robots based on sparse Bayesian learning 被引量:5
5
作者 SHEN Tan DONG YunLong +1 位作者 HE DingXin YUAN Ye 《Science China(Technological Sciences)》 SCIE EI CAS CSCD 2022年第2期386-395,共10页
Nowadays, industrial robots have been widely used in manufacturing, healthcare, packaging, and more. Choosing robots in these applications mainly attributes to their repeatability and precision. However, prolonged and... Nowadays, industrial robots have been widely used in manufacturing, healthcare, packaging, and more. Choosing robots in these applications mainly attributes to their repeatability and precision. However, prolonged and loaded operations can deteriorate the accuracy and efficiency of industrial robots due to the unavoidable accumulated kinematical and dynamical errors. This paper resolves these aforementioned issues by proposing an online time-varying sparse Bayesian learning(SBL) method to identify dynamical systems of robots in real-time. The identification of dynamical systems for industrial robots is cast as a sparse linear regression problem. By constructing the dictionary matrix, the parameters of the robot dynamics are effectively estimated via a re-weighted1-minimization algorithm. Online recursive methods are integrated into SBL to achieve real-time system identification. By including sparsity and promoting online learning, the proposed method can handle time-varying dynamical systems and therefore improve operational stability and accuracy. Experimental results on both simulated and real selective compliance assembly robot arm(SCARA) robots have demonstrated the effectiveness of the proposed method for industrial robots. 展开更多
关键词 industrial robots sparse bayesian learning online identification
原文传递
On fast estimation of direction of arrival for underwater acoustic target based on sparse Bayesian learning 被引量:9
6
作者 WANG Biao ZHU Zhihui DAI Yuewei 《Chinese Journal of Acoustics》 CSCD 2017年第1期102-112,共11页
The Direction of Arrival (DOA) estimation methods for underwater acoustic target using Temporally Multiple Sparse Bayesian Learning (TMSBL) as the reconstructing algorithm have the disadvantage of slow computing s... The Direction of Arrival (DOA) estimation methods for underwater acoustic target using Temporally Multiple Sparse Bayesian Learning (TMSBL) as the reconstructing algorithm have the disadvantage of slow computing speed. To solve this problem, a fast underwater acoustic target direction of arrival estimation was proposed. Analyzing the model characteristics of block-sparse Bayesian learning framework for DOA estimation, an algorithm was proposed to obtain the value of core hyper-parameter through MacKay's fixed-point method to estimate the DOA. By this process, it will spend less time for computation and provide more superior recovery performance than TMSBL algorithm. Simulation results verified the feasibility and effectiveness of the proposed algorithm. 展开更多
关键词 On fast estimation of direction of arrival for underwater acoustic target based on sparse bayesian learning DOA
原文传递
DOA Estimation Based on Root Sparse Bayesian Learning Under Gain and Phase Error 被引量:1
7
作者 Dingke Yu Xin Wang +4 位作者 Wenwei Fang Zixian Ma Bing Lan Chunyi Song Zhiwei Xu 《Journal of Communications and Information Networks》 EI CSCD 2022年第2期202-213,共12页
The direction of arrival(DOA)is approximated by first-order Taylor expansion in most of the existing methods,which will lead to limited estimation accuracy when using coarse mesh owing to the off-grid error.In this pa... The direction of arrival(DOA)is approximated by first-order Taylor expansion in most of the existing methods,which will lead to limited estimation accuracy when using coarse mesh owing to the off-grid error.In this paper,a new root sparse Bayesian learning based DOA estimation method robust to gain-phase error is proposed,which dynamically adjusts the grid angle under coarse grid spacing to compensate the off-grid error and applies the expectation maximization(EM)method to solve the respective iterative formula-based on the prior distribution of each parameter.Simulation results verify that the proposed method reduces the computational complexity through coarse grid sampling while maintaining a reasonable accuracy under gain and phase errors,as compared to the existing methods. 展开更多
关键词 direction of arrival estimation gain-phase error root sparse bayesian learning off-grid error
原文传递
基于吉布斯采样的稀疏水声信道估计方法
8
作者 佟文涛 葛威 +1 位作者 贾亦真 张嘉恒 《哈尔滨工程大学学报(英文版)》 CSCD 2024年第2期434-442,共9页
The estimation of sparse underwater acoustic(UWA)channels can be regarded as an inference problem involving hidden variables within the Bayesian framework.While the classical sparse Bayesian learning(SBL),derived thro... The estimation of sparse underwater acoustic(UWA)channels can be regarded as an inference problem involving hidden variables within the Bayesian framework.While the classical sparse Bayesian learning(SBL),derived through the expectation maximization(EM)algorithm,has been widely employed for UWA channel estimation,it still differs from the real posterior expectation of channels.In this paper,we propose an approach that combines variational inference(VI)and Markov chain Monte Carlo(MCMC)methods to provide a more accurate posterior estimation.Specifically,the SBL is first re-derived with VI,allowing us to replace the posterior distribution of the hidden variables with a variational distribution.Then,we determine the full conditional probability distribution for each variable in the variational distribution and then iteratively perform random Gibbs sampling in MCMC to converge the Markov chain.The results of simulation and experiment indicate that our estimation method achieves lower mean square error and bit error rate compared to the classic SBL approach.Additionally,it demonstrates an acceptable convergence speed. 展开更多
关键词 sparse bayesian learning Channel estimation Variational inference Gibbs sampling
下载PDF
Synthetic aperture radar imaging based on attributed scatter model using sparse recovery techniques
9
作者 苏伍各 王宏强 阳召成 《Journal of Central South University》 SCIE EI CAS 2014年第1期223-231,共9页
The sparse recovery algorithms formulate synthetic aperture radar (SAR) imaging problem in terms of sparse representation (SR) of a small number of strong scatters' positions among a much large number of potentia... The sparse recovery algorithms formulate synthetic aperture radar (SAR) imaging problem in terms of sparse representation (SR) of a small number of strong scatters' positions among a much large number of potential scatters' positions, and provide an effective approach to improve the SAR image resolution. Based on the attributed scatter center model, several experiments were performed with different practical considerations to evaluate the performance of five representative SR techniques, namely, sparse Bayesian learning (SBL), fast Bayesian matching pursuit (FBMP), smoothed 10 norm method (SL0), sparse reconstruction by separable approximation (SpaRSA), fast iterative shrinkage-thresholding algorithm (FISTA), and the parameter settings in five SR algorithms were discussed. In different situations, the performances of these algorithms were also discussed. Through the comparison of MSE and failure rate in each algorithm simulation, FBMP and SpaRSA are found suitable for dealing with problems in the SAR imaging based on attributed scattering center model. Although the SBL is time-consuming, it always get better performance when related to failure rate and high SNR. 展开更多
关键词 attributed scatter center model sparse representation sparse bayesian learning fast bayesian matching pursuit smoothed l0 norm sparse reconstruction by separable approximation fast iterative shrinkage-thresholding algorithm
下载PDF
Data-Driven Discovery of Stochastic Differential Equations 被引量:1
10
作者 Yasen Wang Huazhen Fang +12 位作者 Junyang Jin Guijun Ma Xin He Xing Dai Zuogong Yue Cheng Cheng Hai-Tao Zhang Donglin Pu Dongrui Wu Ye Yuan Jorge Gonçalves Jürgen Kurths Han Ding 《Engineering》 SCIE EI CAS 2022年第10期244-252,共9页
Stochastic differential equations(SDEs)are mathematical models that are widely used to describe complex processes or phenomena perturbed by random noise from different sources.The identification of SDEs governing a sy... Stochastic differential equations(SDEs)are mathematical models that are widely used to describe complex processes or phenomena perturbed by random noise from different sources.The identification of SDEs governing a system is often a challenge because of the inherent strong stochasticity of data and the complexity of the system’s dynamics.The practical utility of existing parametric approaches for identifying SDEs is usually limited by insufficient data resources.This study presents a novel framework for identifying SDEs by leveraging the sparse Bayesian learning(SBL)technique to search for a parsimonious,yet physically necessary representation from the space of candidate basis functions.More importantly,we use the analytical tractability of SBL to develop an efficient way to formulate the linear regression problem for the discovery of SDEs that requires considerably less time-series data.The effectiveness of the proposed framework is demonstrated using real data on stock and oil prices,bearing variation,and wind speed,as well as simulated data on well-known stochastic dynamical systems,including the generalized Wiener process and Langevin equation.This framework aims to assist specialists in extracting stochastic mathematical models from random phenomena in the natural sciences,economics,and engineering fields for analysis,prediction,and decision making. 展开更多
关键词 Data-driven method System identification sparse bayesian learning Stochastic differential equations Random phenomena
下载PDF
Data-driven discovery of linear dynamical systems from noisy data
11
作者 WANG YaSen YUAN Ye +1 位作者 FANG HuaZhen DING Han 《Science China(Technological Sciences)》 SCIE EI CAS CSCD 2024年第1期121-129,共9页
In modern science and engineering disciplines,data-driven discovery methods play a fundamental role in system modeling,as data serve as the external representations of the intrinsic mechanisms within systems.However,e... In modern science and engineering disciplines,data-driven discovery methods play a fundamental role in system modeling,as data serve as the external representations of the intrinsic mechanisms within systems.However,empirical data contaminated by process and measurement noise remain a significant obstacle for this type of modeling.In this study,we have developed a data-driven method capable of directly uncovering linear dynamical systems from noisy data.This method combines the Kalman smoothing and sparse Bayesian learning to decouple process and measurement noise under the expectation-maximization framework,presenting an analytical method for alternate state estimation and system identification.Furthermore,the discovered model explicitly characterizes the probability distribution of process and measurement noise,as they are essential for filtering,smoothing,and stochastic control.We have successfully applied the proposed algorithm to several simulation systems.Experimental results demonstrate its potential to enable linear dynamical system discovery in practical applications where noise-free data are intractable to capture. 展开更多
关键词 system identification sparse bayesian learning Kalman smoothing process and measurement noise
原文传递
Combined UAMP and MF Message Passing Algorithm for Multi-Target Wideband DOA Estimation with Dirichlet Process Prior
12
作者 Shanwen Guan Xinhua Lu +2 位作者 Ji Li Rushi Lan Xiaonan Luo 《Tsinghua Science and Technology》 SCIE EI CAS CSCD 2024年第4期1069-1081,共13页
When estimating the direction of arrival (DOA) of wideband signals from multiple sources, the performance of sparse Bayesian methods is influenced by the frequency bands occupied by signals in different directions. Th... When estimating the direction of arrival (DOA) of wideband signals from multiple sources, the performance of sparse Bayesian methods is influenced by the frequency bands occupied by signals in different directions. This is particularly true when multiple signal frequency bands overlap. Message passing algorithms (MPA) with Dirichlet process (DP) prior can be employed in a sparse Bayesian learning (SBL) framework with high precision. However, existing methods suffer from either high complexity or low precision. To address this, we propose a low-complexity DOA estimation algorithm based on a factor graph. This approach introduces two strong constraints via a stretching transformation of the factor graph. The first constraint separates the observation from the DP prior, enabling the application of the unitary approximate message passing (UAMP) algorithm for simplified inference and mitigation of divergence issues. The second constraint compensates for the deviation in estimation angle caused by the grid mismatch problem. Compared to state-of-the-art algorithms, our proposed method offers higher estimation accuracy and lower complexity. 展开更多
关键词 wideband direction of arrival(DOA)estimation sparse bayesian learning(SBL) unitary approximate message passing(UAMP)algorithm Dirichlet process(DP)
原文传递
基于子空间模型的稀疏贝叶斯DOA估计
13
作者 窦慧晶 郭宏亮 +1 位作者 邢路阳 路瑶 《北京工业大学学报》 CAS 2024年第12期1421-1427,共7页
为了提高相干信源条件下的离格波达方向(direction of arrival,DOA)估计精度,提出一种基于子空间模型的稀疏贝叶斯学习(sparse Bayesian learning,SBL)的DOA估计算法。该算法首先将子空间平滑(subspace smoothing,SS)技术与加权子空间拟... 为了提高相干信源条件下的离格波达方向(direction of arrival,DOA)估计精度,提出一种基于子空间模型的稀疏贝叶斯学习(sparse Bayesian learning,SBL)的DOA估计算法。该算法首先将子空间平滑(subspace smoothing,SS)技术与加权子空间拟合(weighted subspace fitting,WSF)技术结合,然后将此子空间模型应用于SBL算法,并将离散网格点视为动态参数,用期望最大化(expectation maximization,EM)算法迭代更新网格点位置。与传统稀疏恢复算法相比,该算法在估计误差及计算复杂度上均具有明显优势,并对信源数目的估计误差具有较强的鲁棒性。 展开更多
关键词 阵列信号处理 波达方向(direction of arrival DOA)估计 压缩感知 稀疏贝叶斯学习(sparse bayesian learning SBL) 子空间 稀疏恢复
下载PDF
ESTIMATION AND UNCERTAINTY QUANTIFICATION FOR PIECEWISE SMOOTH SIGNAL RECOVERY
14
作者 Victor Churchill Anne Gelb 《Journal of Computational Mathematics》 SCIE CSCD 2023年第2期246-262,共17页
This paper presents an application of the sparse Bayesian learning(SBL)algorithm to linear inverse problems with a high order total variation(HOTV)sparsity prior.For the problem of sparse signal recovery,SBL often pro... This paper presents an application of the sparse Bayesian learning(SBL)algorithm to linear inverse problems with a high order total variation(HOTV)sparsity prior.For the problem of sparse signal recovery,SBL often produces more accurate estimates than maximum a posteriori estimates,including those that useℓ1 regularization.Moreover,rather than a single signal estimate,SBL yields a full posterior density estimate which can be used for uncertainty quantification.However,SBL is only immediately applicable to problems having a direct sparsity prior,or to those that can be formed via synthesis.This paper demonstrates how a problem with an HOTV sparsity prior can be formulated via synthesis,and then utilizes SBL.This expands the class of problems available to Bayesian learning to include,e.g.,inverse problems dealing with the recovery of piecewise smooth functions or signals from data.Numerical examples are provided to demonstrate how this new technique is effectively employed. 展开更多
关键词 High order total variation regularization sparse bayesian learning Analysis and synthesis Piecewise smooth function recovery
原文传递
Multi-dimensional scenario forecast for generation of multiple wind farms 被引量:11
15
作者 Ming YANG You LIN +2 位作者 Simeng ZHU Xueshan HAN Hongtao WANG 《Journal of Modern Power Systems and Clean Energy》 SCIE EI 2015年第3期361-370,共10页
A novel multi-dimensional scenario forecast approach which can capture the dynamic temporal-spatial interdependence relation among the outputs of multiple wind farms is proposed.In the proposed approach,support vector... A novel multi-dimensional scenario forecast approach which can capture the dynamic temporal-spatial interdependence relation among the outputs of multiple wind farms is proposed.In the proposed approach,support vector machine(SVM)is applied for the spot forecast of wind power generation.The probability density function(PDF)of the SVM forecast error is predicted by sparse Bayesian learning(SBL),and the spot forecast result is corrected according to the error expectation obtained.The copula function is estimated using a Gaussian copula-based dynamic conditional correlation matrix regression(DCCMR)model to describe the correlation among the errors.And the multidimensional scenario is generated with respect to the estimated marginal distributions and the copula function.Test results on three adjacent wind farms illustrate the effectiveness of the proposed approach. 展开更多
关键词 Wind power generation forecast Multidimensional scenario forecast Support vector machine(SVM) sparse bayesian learning(SBL) Gaussian copula Dynamic conditional correlation matrix
原文传递
Deep adaptive control with online identification for industrial robots 被引量:2
16
作者 SHEN Tan QIAO XueChun +3 位作者 DONG YunLong WANG YuRan ZHANG Wei YUAN Ye 《Science China(Technological Sciences)》 SCIE EI CAS CSCD 2022年第11期2593-2604,共12页
Derivation of control equations from data is a critical problem in numerous scientific and engineering fields.The inverse dynamic control of robot manipulators in the field of industrial robot research is a key exampl... Derivation of control equations from data is a critical problem in numerous scientific and engineering fields.The inverse dynamic control of robot manipulators in the field of industrial robot research is a key example.Traditionally,researchers needed to obtain the robot dynamic model through physical modeling methods before developing controllers.However,the robot dynamic model and suitable control methods are often elusive and difficult to tune,particularly when dealing with real dynamical systems.In this paper,we combine an enhanced online sparse Bayesian learning(OSBL)algorithm and a model reference adaptive control method to obtain a data-driven modeling and control strategy from data containing noise;this strategy can be applied to dynamical systems.In particular,we use a sparse Bayesian approach,relying only on some prior knowledge of its physics,to extract an accurate mechanistic model from the measured data.Unmodeled parameters are further identified from the modeling error through a deep neural network(DNN).By combining the identification model with a model reference adaptive control approach,a general deep adaptive control(DAC)method is obtained,which can tolerate unmodeled dynamics.The adaptive update law is derived from Lyapunov’s stability criterion,which guarantees the asymptotic stability of the system.Finally,the Enhanced OSBL identification method and DAC scheme are applied on a six-degree-of-freedom industrial robot,and the effectiveness of the proposed method is verified. 展开更多
关键词 industrial robots sparse bayesian learning online identification adaptive control
原文传递
上一页 1 下一页 到第
使用帮助 返回顶部