检索结果-内蒙古大学图书馆

Design of an Adaptive Wideband Beamforming Algorithm for Conformal arrays

IEEE COMMUNICATIONS LETTERS 2014年第11期18卷 1955-1958页

作者： Rasekh, Meysam Seydnejad, Saeid R. Shahid Bahonar Univ Kerman Dept Elect Engn Kerman *** Iran

Wideband conformal arrays are increasingly used in modern communication systems due to their flexibility in attaching to arbitrary surfaces. However, designing such arrays has been limited to pattern synthesis techniques because of the complexity of their geometrical analysis and the different contribution of each sensor in the array response. These techniques are basically non-adaptive data independent methods and are not suitable for dynamic environments. In this letter, the conventional Frost space-time wideband beamforming method and its associated Linearly Constrained Minimum Variance (LCMV) algorithm are employed to develop an adaptive wideband beamforming method for an arbitrarily shaped conformal array. The constraint matrix used in the LCMV algorithm is modified to include the pattern of each individual sensor, thereby incorporating the element responses into the weight adjustment algorithm. A set of angle-frequency constraints are used in the proposed LCMV formulation to achieve the desired frequency-invariant beampattern. It is shown by a comprehensive example of a typical conical array that the desired beampattern can be obtained in an adaptive manner for a time-varying target.

关键词： Conformal array wideband beamforming space-time processing sensor array processing acoustic communication

来源：评论

学校读者我要写书评

暂无评论

ESTIMATING THE NUMBER OF SIGNALS IN THE PRESENCE OF NONUNIFORM NOISE

ESTIMATING THE NUMBER OF SIGNALS IN THE PRESENCE OF NONUNIFO...

引用

IEEE International Conference on Acoustics, Speech and Signal processing (ICASSP)

作者： Diversi, Roberto Guidorzi, Roberto Soverini, Umberto Univ Bologna Dept Elect Elect & Informat Engn DEI I-40136 Bologna Italy

ISBN: (纸本)9781479928934

An important problem in sensor array processing is the estimation of the number of transmitted signals. Most of the proposed solutions rely on the assumption of uniform additive white noise on the measured signals. In this paper, an approach for estimating the number of sources in the presence of nonuniform white noise is proposed. The method is based on the computation of the maximal corank of the covariance matrix of the noisy data in the Frisch scheme context. The effectiveness of the method is tested by means of Monte Carlo simulations.

关键词： Source number estimation sensor array processing nonuniform additive noise Frisch scheme

来源：评论

学校读者我要写书评

暂无评论

MV-PURE ESTIMATOR OF DIPOLE SOURCE SIGNALS IN EEG

MV-PURE ESTIMATOR OF DIPOLE SOURCE SIGNALS IN EEG

引用

IEEE International Conference on Acoustics, Speech, and Signal processing (ICASSP)

作者： Piotrowski, T. Zaragoza-Martinez, C. C. Gutierrez, D. Yamada, I. Nicolaus Copernicus Univ Dept Informat Fac Phys Astron & Informat PL-87100 Torun Poland Ctr Res & Advan Studies Mexico City 66600 DF Mexico Tokyo Inst Technol Dept Commun Engn Tokyo 152 Japan

ISBN: (纸本)9781479903566

We consider the problem of dipole source signals estimation in electroencephalography (EEG) using IDeamforming techniques in ill-conditioned. settings. We take advantage of the link between the linearly constrained minimum-variance (LCMV) beamformer in sensor array processing and the best linear unbiased estimator (BLUE) in linear regression modeling. We show that the recently introduced reducedrank extension of BLUE, named minimum-variance pseudounbiased reduced-rank estimator (MV-PURE), achieves much lower estimation error not only than LCMV beamformer, but also than the previously derived reduced-rank principal components (PC) and cross-spectral metrics (CSM) beamformers in ill-conditioned settings. The practical scenarios where the considered estimation model becomes ill-conditioned are discussed, then we show the applicability of MV-PURE dipole source estimator under those conditions through realistic simulations.

关键词： MV-PURE estimator reduced-rank estimation dipole source signal electroencephalography sensor array processing

来源：评论

学校读者我要写书评

暂无评论

Projection Versus Prewhitening for EEG Interference Suppression

引用

IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING 2012年第5期59卷 1329-1338页

作者： Wu, Shun Chi Swindlehurst, A. Lee Wang, Po T. Nenadic, Zoran Univ Calif Irvine Dept Elect Engn & Comp Sci Irvine CA 92697 USA Univ Calif Irvine Dept Biomed Engn Irvine CA 92697 USA

Suppression of strong, spatially correlated background interference is a challenge associated with electroencephalography (EEG) source localization problems. The most common way of dealing with such interference is through the use of a prewhitening transformation based on an estimate of the covariance of the interference plus noise. This approach is based on strong assumptions regarding temporal stationarity of the data, which do not commonly hold in EEG applications. In addition, prewhitening cannot typically be implemented directly due to ill conditioning of the covariance matrix, and ad hoc regularization is often necessary. Using both simulation examples and experiments involving real EEG data with auditory evoked responses, we demonstrate that a straightforward interference projection method is significantly more robust than prewhitening for EEG source localization.

关键词： Electroencephalography (EEG) interference suppression magnetoencephalography (MEG) sensor array processing source localization

来源：评论

学校读者我要写书评

暂无评论

Subspace Methods for Joint Sparse Recovery

引用

IEEE TRANSACTIONS ON INFORMATION THEORY 2012年第6期58卷 3613-3641页

作者： Lee, Kiryung Bresler, Yoram Junge, Marius Univ Illinois Coordinated Sci Lab Urbana IL 61801 USA Univ Illinois Dept Elect & Comp Engn Urbana IL 61801 USA Univ Illinois Dept Math Urbana IL 61801 USA

We propose robust and efficient algorithms for the joint sparse recovery problem in compressed sensing, which simultaneously recover the supports of jointly sparse signals from their multiple measurement vectors obtained through a common sensing matrix. In a favorable situation, the unknown matrix, which consists of the jointly sparse signals, has linearly independent nonzero rows. In this case, the MUltiple SIgnal Classification (MUSIC) algorithm, originally proposed by Schmidt for the direction of arrival estimation problem in sensor array processing and later proposed and analyzed for joint sparse recovery by Feng and Bresler, provides a guarantee with the minimum number of measurements. We focus instead on the unfavorable but practically significant case of rank defect or ill-conditioning. This situation arises with a limited number of measurement vectors, or with highly correlated signal components. In this case, MUSIC fails and, in practice, none of the existing methods can consistently approach the fundamental limit. We propose subspace-augmented MUSIC (SA-MUSIC), which improves on MUSIC such that the support is reliably recovered under such unfavorable conditions. Combined with a subspace-based greedy algorithm, known as Orthogonal Subspace Matching Pursuit, which is also proposed and analyzed in this paper, SA-MUSIC provides a computationally efficient algorithm with a performance guarantee. The performance guarantees are given in terms of a version of the restricted isometry property. In particular, we also present a non-asymptotic perturbation analysis of the signal subspace estimation step, which has been missing in the previous studies of MUSIC.

关键词： Compressed sensing joint sparsity multiple measurement vectors (MMV) restricted isometry property (RIP) sensor array processing spectrum-blind sampling subspace estimation

来源：评论

学校读者我要写书评

暂无评论

Efficient Dipole Parameter Estimation in EEG Systems With Near-ML Performance

引用

IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING 2012年第5期59卷 1339-1348页

作者： Wu, Shun Chi Swindlehurst, A. Lee Wang, Po T. Nenadic, Zoran Univ Calif Irvine Dept Elect Engn & Comp Sci Irvine CA 92697 USA Univ Calif Irvine Dept Biomed Engn Irvine CA 92697 USA

Source signals that have strong temporal correlation can pose a challenge for high-resolution EEG source localization algorithms. In this paper, we present two methods that are able to accurately locate highly correlated sources in situations where other high-resolution methods such as multiple signal classification and linearly constrained minimum variance beamforming fail. These methods are based on approximations to the optimal maximum likelihood (ML) approach, but offer significant computational advantages over ML when estimates of the equivalent EEG dipole orientation and moment are required in addition to the source location. The first method uses a two-stage approach in which localization is performed assuming an unstructured dipole moment model, and then the dipole orientation is obtained by using these estimates in a second step. The second method is based on the use of the noise subspace fitting concept, and has been shown to provide performance that is asymptotically equivalent to the direct ML method. Both techniques lead to a considerably simpler optimization than ML since the estimation of the source locations and dipole moments is decoupled. Examples using data from simulations and auditory experiments are presented to illustrate the performance of the algorithms.

关键词： Electroencephalography (EEG) magnetoencephalography (MEG) sensor array processing source localization

来源：评论

学校读者我要写书评

暂无评论

A New Method of Regularization Parameter Estimation for Source Localization

A New Method of Regularization Parameter Estimation for Sour...

引用

2011 IEEE CIE International Conference on Radar(2011年IEEE国际雷达会议RADAR 2011)

作者： Juanru Huang Mei Dong Shili Li National Key Lab for Radar Signal Processing Xidian Univ. Xi’an 710071 China

ISBN: (纸本)9781424484447

The problem of estimating the regularization parameter for source localization in sparse-regularization framework is considered in this paper. We employ the distribution about every entry of the square of the Frobenius norm of noise to obtain a larger and more appropriate regularization parameter. The paper analyzes the reason that we can not simply set it equal to the square of the Frobenius norm of noise and presents the estimation in two practical cases: one works without taking singular value decomposition (SVD) of sensor outputs;the other works after that pretreatment for large data quantity. The simulation results demonstrate that the proposed method has many advantages, including enhancing resolution, effectively suppressing spurious peaks, improving robustness to noise, as well as increasing the number of resolvable sources.

关键词： sparse representation regularization parameter estimation source localization sensor array processing

来源：评论

学校读者我要写书评

暂无评论

Direction-of-Arrival Estimation Using a Mixed l_2,0 Norm Approximation

引用

IEEE TRANSACTIONS ON SIGNAL processing 2010年第9期58卷 4646-4655页

作者： Hyder, Md Mashud Mahata, Kaushik Univ Newcastle Dept Elect Engn Callaghan NSW 2308 Australia

A set of vectors is called jointly sparse when its elements share a common sparsity pattern. We demonstrate how the direction-of-arrival (DOA) estimation problem can be cast as the problem of recovering a joint-sparse representation. We consider both narrowband and broadband scenarios. We propose to minimize a mixed l(2,0) norm approximation to deal with the joint-sparse recovery problem. Our algorithm can resolve closely spaced and highly correlated sources using a small number of noisy snapshots. Furthermore, the number of sources need not be known a priori. In addition, our algorithm can handle more sources than other state-of-the-art algorithms. For the broadband DOA estimation problem, our algorithm allows relaxing the half-wavelength spacing restriction, which leads to a significant improvement in the resolution limit.

关键词： Compressive sampling direction-of-arrival (DOA) joint-sparse multiple measurement vectors sensor array processing sparse representation

来源：评论

学校读者我要写书评

暂无评论

High-Resolution Direction Finding for MIMO Radar Systems Without Eigendecomposition

High-Resolution Direction Finding for MIMO Radar Systems Wit...

引用

2011 International conference on Instrumentation,Measurement,Computer,Communication and Control (IMCCC2011)(第一届仪器测量、计算机通信与控制国际会议)

作者： Lei Huang Siliang Wu Department of Electronic Engineering Harbin Institute of Technology Shenzhen Graduate School Department of Electronic Engineering Beijing Institute of Technology

ISBN: (纸本)9781457713675

By simultaneously transmitting and receiving mul- tiple coded waveforms with multiple-input multiple-output (MIMO) configuration,the MIMO radar appears more at- tractive than the traditional phased-array radar in perfor- ***,when applied in the MIMO radar system,the classical subspace-based methods for high-resolution DOA estimation are computationally prohibited as the observed covariance matrix is the Krvnecker product of the transmit and rcccivc covariancc matriccs,considcrably incrcasing the sizc of the covariance *** cure this problem,a computationally efficient subspace-based method for DOA estimation is ad- dressed in this *** proposed method only needs vector- vector operations,and does not involve the covariance matrix calculation and its EVD or inversion ***,the proposed method is computationally attractive for practical *** results are included to illustrate the performance of the proposed method.

关键词： sensor array processing multiple-input multiple-output (MIMO) radar direction of arrival (DOA)

来源：评论

学校读者我要写书评

暂无评论

MMSE-Based MDL Method for Robust Estimation of Number of Sources Without Eigendecomposition

引用

IEEE TRANSACTIONS ON SIGNAL processing 2009年第10期57卷 4135-4142页

作者： Huang, Lei Long, Teng Mao, Erke So, H. C. Beijing Inst Technol Dept Elect Engn Beijing 100081 Peoples R China City Univ Hong Kong Dept Elect Engn Kowloon Hong Kong Peoples R China

It is well known that the conventional eigenvalue-based minimum description length (MDL) approach for source number estimation suffers from high computational load and performs optimally only in the presence of spatially and temporally white noise. To improve the robustness of the MDL methodology, we propose to utilize the minimum mean square error (MMSE) of the multistage Wiener filter to calculate the required description length for encoding the observed data, instead of relying on the eigenvalues of the data covariance matrix. As there is no need to calculate the covariance matrix and its eigenvalue decomposition, our derived MMSE-based MDL (mMDL) method is also more computationally efficient than the traditional counterparts. Numerical examples are included to demonstrate the robustness of the mMDL detector in nonuniform noise.

关键词： Eigenvalue decomposition (EVD) minimum description length (MDL) multistage Wiener filter (MSWF) sensor array processing source number estimation

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：