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检索条件"任意字段=2008 IEEE Sensor Array and Multichannel Signal Processing Workshop"

共 1267 条记录，以下是511-520 订阅

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Multi-task wireless acoustic sensor network for node-specific speech enhancement and DOA estimation

Multi-task wireless acoustic sensor network for node-specifi...

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sensor array and multichannel signal processing workshop

作者： Amin Hassani Jorge Plata-Chaves Alexander Bertrand Marc Moonen Department of Electrical Engineering-ESAT KU Leuven Leuven Belgium

ISBN: (纸本)9781509021048

We consider the design of a distributed algorithm that is suitable for a wireless acoustic sensor network formed by nodes solving multiple tasks (MDMT). In the network, some of the nodes aim at estimating the node-specific direction-of-arrival of some desired sources. Additionally, there are other nodes that aim at implementing either a multi-channel Wiener filter or a minimum variance distortionless response beamformer in order to estimate node-specific desired signals as they impinge on their microphones. By using compressive filter-and-sum operations that incorporate a low-rank approximation of the sensor signal correlation matrix, the proposed MDMT algorithm let the nodes cooperate to achieve the network-wide centralized solution of their node-specific estimation problems without any knowledge about the tasks of other nodes. Finally, the effectiveness of the algorithm is shown through computer simulations.

关键词： Estimation Microphones Speech signal processing algorithms Direction-of-arrival estimation Wireless sensor networks Wireless communication

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Single snapshot DOA estimation in the presence of mutual coupling for arbitrary array structures

Single snapshot DOA estimation in the presence of mutual cou...

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sensor array and multichannel signal processing workshop

作者： Ahmet M. Elbir T. Engin Tuncer Dept. of Electrical and Electronics Engineering Middle East Technical University Ankara TURKEY

ISBN: (纸本)9781509021048

In this paper, single snapshot direction-of-arrival (DOA) estimation under mutual coupling (MC) is considered with arbitrary array structures. A compressed sensing approach is utilized and a joint-sparse recovery algorithm is proposed. In this respect, both spatial source directions and MC coefficients are embedded into a joint-sparse vector. A new dictionary matrix is defined using the symmetricity of MC matrix. The proposed approach does not depend on the structure of MC matrix and it is suitable for any type of array geometry. In the simulations, the proposed method is evaluated for a planar array where the antennas are placed randomly in 2-D space. It is shown that the proposed method effectively estimates the source parameters and performs significantly better than the alternative methods.

关键词： Symmetric matrices Direction-of-arrival estimation Estimation Antenna arrays Transmission line matrix methods Dictionaries

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Efficient linearized Bregman iteration for sparse adaptive filters and Kaczmarz solvers

Efficient linearized Bregman iteration for sparse adaptive f...

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sensor array and multichannel signal processing workshop

作者： Michael Lunglmayr Mario Huemer Institute of Signal Processing Johannes Kepler University Linz Austria

ISBN: (纸本)9781509021048

Linearized Bregman iterations are low complexity and high precision approaches for solving the combined l 1 /l 2 minimization problem. In this work we give a derivation of the linearized Bregman iteration and show the links to Kaczmarz's algorithm as well as to sparse least mean squares (LMS) filters. We present a novel extension allowing to perform combined l 1 /l 2 minimization either in an LMS based adaptive filter or in a Kaczmarz based batch solution. By means of simulations we demonstrate that the performance of our extension is comparable to the original linearized Bregman approaches. Furthermore, we show that with this extension l 1 /l 2 minimization can be performed with less complexity than the corresponding l 2 minimization.

关键词： Minimization Complexity theory Simulation Algorithm design and analysis signal processing algorithms Indexes Sparse matrices

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A robust beamformer with main beam control

A robust beamformer with main beam control

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sensor array and multichannel signal processing workshop

作者： Bin Liao Chongtao Guo Lei Huang Qiang Li H. C. So College of Information Engineering Shenzhen University Shenzhen China Department of Electronic Engineering City University of Hong Kong Kowloon Hong Kong SAR China

ISBN: (纸本)9781509021048

In this paper, a new robust beamforming approach which is capable of accurately controlling the main beam response is proposed. In this method, steering vector uncertainties are taken into account in the beamformer design problem with array magnitude response constraints. This allows us to control the main beam as prescribed. However, the resultant non-convex problem has a different formulation from that of the existing methods for robust beamforming with magnitude response constraints. Thus, existing techniques cannot be applied directly. To cope with this problem, the lower and upper norm bounds of the beamformer weight vector are first derived. The semidefinite relaxation technique is then employed as an approximate solver ending up with a grid search solution. Simulation results show that the proposed method is able to accurately control the main beam magnitude response in the presence of steering vector uncertainties.

关键词： Uncertainty Robustness array signal processing Covariance matrices Linear programming sensor arrays Electronic mail

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Mixed source prior for the fast independent vector analysis algorithm

Mixed source prior for the fast independent vector analysis ...

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sensor array and multichannel signal processing workshop

作者： Waqas Rafique Syed Mohsen Naqvi Jonathon A. Chambers Communications Sensors Newcastle University UK

ISBN: (纸本)9781509021048

A novel method to improve the separation performance and the convergence speed of fast independent vector analysis (FastIVA) for frequency domain operation is proposed. In this paper, a mixture of super Gaussian distributions is adopted as a source prior for the FastIVA algorithm. In this mixed source prior, a Student's t distribution is adopted to model the high amplitude information in the spectrum of a speech signal and another super Gaussian distribution with less heavy tails is used to model the rest of the information. Moreover, in the proposed mixed source prior the weight of both distributions can be varied according to the frequency dependent amplitude of the speech signals. The performance of the proposed algorithm is demonstrated by using the imaging method and binaural room impulse responses and comparison is made with the FastIVA using the original super Gaussian source prior.

关键词： Speech Gaussian distribution Algorithm design and analysis signal processing algorithms Imaging Mathematical model Convergence

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Two-dimensional DOA estimation using parallel coprime subarrays

Two-dimensional DOA estimation using parallel coprime subarr...

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sensor array and multichannel signal processing workshop

作者： Si Qin Yimin D. Zhang Moeness G. Amin Villanova University Villanova PA US Department of Electrical and Computer Engineering Temple University Philadelphia PA USA Center for Advanced Communications Villanova University Villanova PA USA

ISBN: (纸本)9781509021048

A conventional coprime array is a linear array, which consists of two uniform linear subarrays to construct an effective difference coarray with certain desirable characteristics. In this paper, we propose a parallel coprime array structure and a novel algorithm for two-dimensional (2-D) direction-of-arrival (DOA) estimation. By vectorizing the cross-covariance matrix of subarray data, the resulting virtual difference coarray enables resolving more signals than the number of antennas. The 2-D DOA estimation problem is cast as two separate one-dimensional DOA estimation problems, where the estimated azimuth and elevation angles can be properly associated. Compared with other methods, such as, the propagator method (PM) and the rank-reduction (RARE) based algorithms, the proposed method resolves more signals and achieves improved estimation performance.

关键词： Estimation Direction-of-arrival estimation sensor arrays signal resolution Covariance matrices

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Improving spectral efficiency in large-array FDD systems with hybrid beamforming

Improving spectral efficiency in large-array FDD systems wit...

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sensor array and multichannel signal processing workshop

作者： Daniel C. Araújo Eleftherios Karipidis André L. F. de Almeida João C. M. Mota Group of Wireless Communication Research (GTEL) Ceará Brazil Ericsson Research Stockholm Sweden

We investigate the spectral efficiency of large-array systems serving a single-antenna user in cellular frequencies, using full digital or hybrid analog-digital beamforming in frequency division duplexing (FDD) or time division duplexing (TDD) mode. We consider downlink transmission in time-frequency coherent channel blocks, containing both the pilots and data. Considering FDD and taking the pilot overhead into account, we show that hybrid beamforming provides better spectral efficiency performance than full digital beamforming, mainly in cases with high mobility and many antenna elements. We also optimize the number of used radio chains for hybrid beamforming in FDD and show it is possible to achieve comparable spectral efficiency as with full digital beamforming in TDD.

关键词： array signal processing Radio frequency OFDM Antennas MIMO Channel estimation Time-frequency analysis

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Privacy-aware decentralized detection using linear precoding

Privacy-aware decentralized detection using linear precoding

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sensor array and multichannel signal processing workshop

作者： Xin He Wee Peng Tay Meng Sun Department of Electrical and Electronic Engineering Nanyang Technological University Singapore

ISBN: (纸本)9781509021048

We study a nonparametric decentralized detection problem in which sensors send information to a fusion center that uses a support vector machine to make decisions about a public hypothesis. However, the same sensor information may also be used by the fusion center to infer about a private hypothesis, which the sensors wish to protect. To ensure information privacy (as opposed to data privacy), sensors perform linear precoding on their data. We develop an algorithm to optimize the precoder matrices in order to ensure that the empirical risk of detecting the private hypothesis is above a given threshold, while minimizing the empirical regularized risk of detecting the public hypothesis. Simulation results with both synthetic and real data sets demonstrate that our approach is able to ensure information privacy.

关键词： sensor fusion Data privacy Precoding Optimization Support vector machines Privacy

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Serial-inspired diffusion based on message passing for distributed estimation in adaptive networks

Serial-inspired diffusion based on message passing for distr...

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sensor array and multichannel signal processing workshop

作者： Cornelius T. Healy Rodrigo C. de Lamare CETUC - Pontifical Catholic University of Rio de Janeiro - PUC-RJ Rio de Janeiro Brazil

ISBN: (纸本)9781509021048

Distributed estimation and processing in networks modeled by graphs have received a great deal of interest recently, due to the benefits of decentralised processing in terms of performance and robustness to communications link failure between nodes of the network. Diffusion-based algorithms have been demonstrated to be among the most effective for distributed signal processing problems, through the combination of local node estimate updates and sharing of information with neighbour nodes through diffusion. In this work, we develop a serial-inspired approach based on message-passing strategies that provides a significant improvement in performance over prior art. The concept of serial processing in the graph has been successfully applied in sum-product based algorithms and here provides inspiration for an algorithm which makes use of the most up-to-date information in the graph in combination with the diffusion approach to offer improved performance.

关键词： Estimation Convergence signal processing algorithms Indexes Schedules Cost function Complexity theory

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Shift invariance, incomplete arrays and coupled CPD: A case study

Shift invariance, incomplete arrays and coupled CPD: A case ...

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sensor array and multichannel signal processing workshop

作者： Mikael Sørensen Lieven De Lathauwer KU Leuven Kulak Engineering and Technology Kortrijk Belgium

Tensors have proven to be useful tools for array processing. Most attention has been paid to separable arrays, which lead to a Canonical Polyadic Decomposition (CPD). For more general geometries, and in particular for sparse arrays and arrays with missing sensors, more general tensor methods are required. The recently proposed coupled CPD framework allows a data fission/fusion approach in which one zooms in on partial structures and combines the partial CPDs through which the latter are imposed. This approach yields explicit algebraic conditions under which the solution is unique. The exact solution can be found with a matrix eigenvalue decomposition in the noiseless case, similar to ESPRIT in the case of uniform linear arrays. We study in detail the case of sparse spatial sampling where sensors are located on points of a two-dimensional grid. Despite the fact that the array is incomplete, coupled CPD allows us to exploit the rectangularity of the grid as well as the uniformity of the spatial sampling in both dimensions.

关键词： Tensile stress sensor arrays Matrix decomposition Antenna arrays Direction-of-arrival estimation

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