检索结果-内蒙古大学图书馆

IEEE Radar Conference (RadarConf)

作者： Zhao, Yonghong Zhang, Linrang Zhang, Juan Guo, Yumei Gu, Yabin Xidian Univ Dept Elect Engn Natl Lab Radar Signal Proc Xian 710071 Shaanxi Peoples R China

ISBN: (纸本)9781467388238

A low complexity method for wideband direction finding based on sparse representation is proposed, which combines the rotational signal subspace (RSS) with weighted subspace fitting (WSF) to improve the performance of DOA estimation. Exploiting the result of the focusing operation, the covariance matrix at the focusing frequency can be obtained and used as the data for sparse recovery to get wideband DOA estimates. The WSF is employed to reduce the sensitivity to the noise and the regularization parameter is given by the asymptotic distribution of the WSF criterion. Simulations are provided to prove the efficiency and performance of the proposed method.

关键词： direction-of-arrival (DOA) estimation sparse representation sensor array processing

来源：评论

学校读者我要写书评

暂无评论

Blind Calibration of sensor arrays for Narrowband Signals with Asymptotically Optimal Weighting 27

Blind Calibration of Sensor Arrays for Narrowband Signals wi...

引用

27th European Signal processing Conference (EUSIPCO)

作者： Weiss, Amir Yeredor, Arie Tel Aviv Univ Sch Elect Engn Tel Aviv Israel

ISBN: (纸本)9789082797039

We revisit the problem of blind calibration of uniform linear sensors arrays for narrowband signals and set the premises for the derivation of the optimal blind calibration scheme. In particular, instead of taking the direct (rather involved) Maximum Likelihood (ML) approach for joint estimation of all the unknown model parameters, we follow Paulraj and Kailath's classical approach in exploiting the special (Toeplitz) structure of the observed covariance. However, we offer a substantial improvement over Paulraj and Kailath's Least Squares (LS) estimate by using asymptotic approximations in order to obtain simple, (quasi-)linear Weighted LS (WLS) estimates of the sensors' gains and phases offsets with asymptotically optimal weighting. As we show in simulation experiments, our WLS estimates exhibit near-optimal performance, with a considerable improvement (reaching an order of magnitude and more) in the resulting mean squared errors, w.r.t. the corresponding ordinary LS estimates. We also briefly explain how the methodology derived in this work may be utilized in order to obtain (by certain modifications) the asymptotically optimal ML estimates w.r.t. the raw data via a (quasi)-linear WLS estimate.

关键词： sensor array processing gain estimation phase estimation self-calibration weighted least squares

来源：评论

学校读者我要写书评

暂无评论

EFFECTS OF UNKNOWN NOISE COVARIANCE ON PARAMETRIC array-processing ALGORITHMS

EFFECTS OF UNKNOWN NOISE COVARIANCE ON PARAMETRIC ARRAY-PROC...

引用

9TH SYMP ON IDENTIFICATION AND SYSTEM PARAMETER ESTIMATION 1991

作者： VIBERG, M

来源：评论

学校读者我要写书评

暂无评论

Subspace-based interference removal methods for a multichannel biomagnetic sensor array

引用

JOURNAL OF NEURAL ENGINEERING 2017年第5期14卷 051001-051001页

作者： Sekihara, Kensuke Nagarajan, Srikantan S. Signal Anal Inc Hachioji Tokyo Japan Tokyo Med & Dent Univ Dept Adv Technol Med Bunkyo Ku 1-5-45 Yushima Tokyo 1138519 Japan Univ Calif San Francisco Biomagnet Imaging Lab 513 Parnassus AveS362 San Francisco CA 94143 USA

Objective. In biomagnetic signal processing, the theory of the signal subspace has been applied to removing interfering magnetic fields, and a representative algorithm is the signal space projection algorithm, in which the signal/interference subspace is defined in the spatial domain as the span of signal/interference-source lead field vectors. This paper extends the notion of this conventional (spatial domain) signal subspace by introducing a new definition of signal subspace in the time domain. Approach. It defines the time-domain signal subspace as the span of row vectors that contain the source time course values. This definition leads to symmetric relationships between the time-domain and the conventional (spatial-domain) signal subspaces. As a review, this article shows that the notion of the time-domain signal subspace provides useful insights over existing interference removal methods from a unified perspective. Main results and significance. Using the time-domain signal subspace, it is possible to interpret a number of interference removal methods as the time domain signal space projection. Such methods include adaptive noise canceling, sensor noise suppression, the common temporal subspace projection, the spatio-temporal signal space separation, and the recently-proposed dual signal subspace projection. Our analysis using the notion of the time domain signal space projection reveals implicit assumptions these methods rely on, and shows that the difference between these methods results only from the manner of deriving the interference subspace. Numerical examples that illustrate the results of our arguments are provided.

关键词： interference removal magnetoencephalography sensor array processing signal subspace biomagnetic imaging biomagnetism multi-sensor array

来源：评论

学校读者我要写书评

暂无评论

Structured autocorrelation matrix estimation for coprime arrays

引用

SIGNAL processing 2021年 183卷 107987-107987页

作者： Chachlakis, Dimitris G. Markopoulos, Panos P. Rochester Inst Technol Dept Elect & Microelect Engn Rochester NY 14623 USA

A coprime array receiver processes a collection of received-signal snapshots to estimate the autocorrelation matrix of a larger (virtual) uniform linear array, known as coarray. By the received-signal model, this matrix has to be (i) Positive Definite, (ii) Hermitian, (iii) Toeplitz, and (iv) its noise-subspace eigen-values have to be equal. Existing coarray autocorrelation matrix estimates satisfy a subset of the above conditions. In this work, we propose an optimization framework which offers a novel estimate satisfying all four conditions: we propose to iteratively solve a sequence of distinct structure-optimization problems and show that, upon convergence, we provably obtain a single estimate satisfying (i)-(iv). Numerical studies illustrate that the proposed estimate outperforms standard counterparts, both in autocorrelation matrix estimation error and Direction-of-Arrival estimation. (C) 2021 Published by Elsevier B.V.

关键词： sensor array processing Coprime arrays Coarray Autocorrelation estimation

来源：评论

学校读者我要写书评

暂无评论

Effects of Unknown Noise Covariance on Parametric array processing Algorithms

引用

IFAC Proceedings Volumes 1992年第15期25卷 393-398页

作者： M. Viberg Department of Electrical Engineering Linköping University S-581 83 Linköping Sweden

Signal parameter estimation from measurements on a sensor array is an important problem in many engineering applications. Recently, there has been a large interest in parametric methods in the literature. An important assumption in essentially all of these methods is that the spatial correlation structure of the background noise (i.e., the correlation from sensor to sensor) is known to within a multiplicative scalar. In practice, this is often achieved by measuring the array соvariance when no signals are present. This results unavoidably in errors in the noise model. In this paper, the effect of such model errors on parametric methods are examined. The methods in question are the deterministic and stochastic maximum likelihood methods, and the so-called weighted subspace fitting technique. First-order expressions for the mean square etror (MSE) of the parameter estimates are derived. The spatial noise correlation structures that lead to maximum performance loss are identified under different assumptions. In case of high signal to noise ratio (SNR), it is found that the MSE can be increased by a factor m , where m is the number of sensors in the array, as compared to spatially white noise. Simple expressions comparing the asymptotic (for large amounts of data) bias, resulting from a small noise covariance perturbation, with the asymptotic standard deviation are derived. Numerical examples are included to illustrate the obtained results.

关键词： sensor array processing parameter estimation sensitivity analysis error analysis modeling signal processing

来源：评论

学校读者我要写书评

暂无评论

Large-Sample Analysis of Music and Min-Norm Direction Estimators in the Presence of Model Errors

引用

IFAC Proceedings Volumes 1994年第8期27卷 125-130页

作者： A. Kangas P. Stoica T. Söderström CelsiusTech Systems AB 5-175 88 Järfälla Sweden Uppsala University Systems and Control Group Box 27 S-751 03 Uppsala Sweden

We analyze the large-sample mean square error (MSE) of MUSIC and Min-Norm direction-of-arrival (DOA) estimators under fairly general conditions, including mismodelling of the array response and the noise covariance. We separate the contributions to the MSE into a bias part caused by modelling errors and a variance part caused by finite (yet large) sample effects. The bias is simply evaluated by comparing the limiting estimate (corresponding to an infinite number of snapshots) with the true DOA’s (which are known to the analyzer). To simplify the variance derivation we assume that the snapshots are complex i.i.d. Gaussian vectors and that the largest eigenvalues of their covariance matrix are distinct; but, otherwise, make none of the assumptions commonly used in previous analyses; in particular we do not constrain the snapshots to satisfy any model equation. The theoretical results obtained are illustrated by means of numerical examples using various modelling errors.

关键词： sensor array processing parameter estimation signal processing sensitivity analysis

来源：评论

学校读者我要写书评

暂无评论

Underdetermined DOA Estimation Using MVDR-Weighted LASSO

引用

sensorS 2016年第9期16卷 1549-1549页

作者： Salama, Amgad A. Ahmad, M. Omair Swamy, M. N. S. Concordia Univ Dept Elect & Comp Engn Montreal PQ H3G 1M8 Canada

The direction of arrival (DOA) estimation problem is formulated in a compressive sensing (CS) framework, and an extended array aperture is presented to increase the number of degrees of freedom of the array. The ordinary least square adaptable least absolute shrinkage and selection operator (OLS A-LASSO) is applied for the first time for DOA estimation. Furthermore, a new LASSO algorithm, the minimum variance distortionless response (MVDR) A-LASSO, which solves the DOA problem in the CS framework, is presented. The proposed algorithm does not depend on the singular value decomposition nor on the orthogonality of the signal and the noise subspaces. Hence, the DOA estimation can be done without a priori knowledge of the number of sources. The proposed algorithm can estimate up to ((M2 2) /2 + M 1) /2 sources using M sensors without any constraints or assumptions about the nature of the signal sources. Furthermore, the proposed algorithm exhibits performance that is superior compared to that of the classical DOA estimation methods, especially for low signal to noise ratios (SNR), spatially-closed sources and coherent scenarios.

关键词： adaptable LASSO sparse array direction of arrival estimation compressive sensing sensor array processing

来源：评论

学校读者我要写书评

暂无评论

PERFORMANCE ANALYSIS OF ROOT-MUSIC-BASED DIRECTION-OF-ARRIVAL ESTIMATION FOR ARBITRARY NON-UNIFORM arrayS

PERFORMANCE ANALYSIS OF ROOT-MUSIC-BASED DIRECTION-OF-ARRIVA...

引用

2008 IEEE sensor array and Multichannel Signal processing Workshop

作者： Michael Rubsamen Alex B. Gershman Communication Systems Group Technische Universität Darmstadt Darmstadt Germany

We present the asymptotic performance analysis of the interpolated root-MUSIC and manifold separation (MS) techniques for direction-of-arrival (DOA) estimation in arbitrary non-uniform sensor arrays. Our analysis take... 详细信息

关键词： Direction-of-arrival estimation Non-uniform sensor arrays Root-MUSIC sensor array processing

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

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：