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检索条件"任意字段=2016 IEEE Sensor Array and Multichannel Signal Processing Workshop, SAM 2016"

共 1002 条记录，以下是61-70 订阅

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Strategies for principal component analysis in wireless sensor networks

Strategies for principal component analysis in wireless sens...

引用

8th ieee sensor array and multichannel signal processing workshop (sam)

作者： Ghadban, Nisrine Honeine, Paul Francis, Clovis Mourad-Chehade, Farah Farah, Joumana Univ Libanaise Fac Genie Beirut Lebanon Univ Technol Troyes CNRS Inst Charles Delaunay Troyes France Holy Spirit Univ Kaslik Fac Engn Telecommun Dept Beirut Lebanon

ISBN: (纸本)9781479914814

This paper deals with the issue of monitoring physical phenomena using wireless sensor networks. It provides principal component analysis for the time series of sensors' measurements. Without the need to compute the sample covariance matrix, we derive several in-network strategies to estimate the principal axis, including noncooperative and diffusion strategies. The performance of the proposed strategies is illustrated in the issue of monitoring gas diffusion.

关键词： Principal component analysis wireless sensor network adaptive learning distributed processing

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Computationally Efficient Waveform Design in Spectrally Dense Environment 10

Computationally Efficient Waveform Design in Spectrally Dens...

引用

10th ieee sensor array and multichannel signal processing workshop (sam)

作者： Yli-Niemi, Markus Vorobyov, Sergiy A. Aalto Univ Dept Signal Proc & Acoust POB 15400 FI-00076 Aalto Finland

ISBN: (纸本)9781538647523

The problem of unimodular radar waveform design with similarity constraint in spectrally dense environment is considered. The corresponding optimization problem is non-convex and additionally large-scale. Indeed, the waveform length can be of several thousands and the waveform has to be designed in every coherent processing interval. Since the problem is non-convex, the majorization-minimization (MaMi) method is used for addressing it, and since it is large-scale, the alternating direction method of multipliers (ADMM) is adopted. Thus, we develop a computationally efficient approach for solving the problem by using MaMi with proper design of majorization function together with alternating ADMM with newly proposed computationally efficient proximal projections. We evaluate the computational cost of the proposed algorithm and show its fast convergence unmatched by existing approaches in terms of simulations.

关键词： Radar signal processing algorithms Optimization array signal processing sensor arrays Conferences"

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Distributed Detection and Imaging in MIMO Radar Network Based on Averaging Consensus 10

Distributed Detection and Imaging in MIMO Radar Network Base...

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10th ieee sensor array and multichannel signal processing workshop (sam)

作者： Zabolotsky, Anton A. Mavrychev, Evgeny A. Nizhnii Novgorod State Tech Univ Dept Informat Radio Syst 24 Minin St Nizhnii Novgorod Russia

ISBN: (纸本)9781538647523

In this paper, we consider distributed signal processing in a MIMO radar network. We suppose that transmitting and receiving nodes of MIMO radar are distributed in a large-scale area. In this network the neighboring receiving nodes communicate with each other to exchange data. A fully distributed algorithm for detection and imaging is proposed. This algorithm is based on the averaging consensus approach. The effectiveness of the proposed technique is confirmed by numerical examples. It is compared to the centralized solution in a fusion center, the local solution in an individual receiving node and the proposed distributed algorithm.

关键词： MIMO radar network distributed signal processing averaging consensus algorithm

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2D DOA Estimation for Uniform Rectangular array With One-bit Measurement 11

2D DOA Estimation for Uniform Rectangular Array With One-bit...

引用

ieee 11th sensor array and multichannel signal processing workshop (sam)

作者： Xiong, Yang Li, Zeyang Wen, Fangqing Yangtze Univ Sch Elect & Informat Jingzhou Peoples R China

ISBN: (纸本)9781728119465

Direction-of-arrival (DOA) estimation is an interesting research topic with various applications. Existing algorithms provide superior estimation performance, at the cost of accurate quantified measurements. In this paper, we stress the problem of 2D DOA estimation for uniform rectangular array using one-bit measurements. The relationship between the covariance matrices of one-bit measurement and that of the accurately quantified measurement is analyzed in detail, from which we find the existing tensor algorithm can be directly applied. As a result, a one-bit parallel factor analysis (PARAFAC) estimator is proposed. Simulation results show the effectiveness of the proposed method.

关键词： One-bit quantification array signal processing parallel factor analysis (PARAFAC) 2D DOA estimation

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Localization of Multiple Simultaneously Active Speakers in an Acoustic sensor Network 10

Localization of Multiple Simultaneously Active Speakers in a...

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10th ieee sensor array and multichannel signal processing workshop (sam)

作者： Brendel, Andreas Gannot, Sharon Kellermann, Walter Friedrich Alexander Univ Erlangen Nurnberg Multimedia Commun & Signal Proc Cauerstr 7 D-91058 Erlangen Germany Bar Ilan Univ Fac Engn IL-5290002 Ramat Gan Israel

ISBN: (纸本)9781538647523

This paper addresses the localization of an unknown number of acoustic sources in an enclosure. We extend a well established algorithm for localization of acoustic sources, which is based on the Expectation Maximization (EM) algorithm for clustering of phase differences by a Gaussian mixture model. Supporting a more appropriate probabilistic model for spherical data such as direction of arrival or phase differences, the von Mises distribution is used to derive a localization algorithm for multiple simultaneously active sources. Experiments with simulated room impulse responses confirm the superiority of the proposed algorithm to the existing method in terms of localization performance.

关键词： Microphones Direction-of-arrival estimation signal processing algorithms Time-frequency analysis Acoustics Indexes array signal processing"

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Tracking time-varying mixing system in blind separation

Tracking time-varying mixing system in blind separation

引用

1st ieee sensor array and multichannel signal processing workshop (sam 2000)

作者： Enescu, M Koivunen, V Helsinki Univ Technol Signal Proc Lab FIN-02015 Helsinki Finland

ISBN: (纸本)0780363396

This paper addresses the problem of blind separation from time varying noisy mixtures. The goal is to adapt to changes that occur in the mixing system as well as to estimate if the number of sources change over time. This is achieved by using an adaptive predictor corrector structure and subspace tracker. Changes in the array covariance matrix are used in revealing changes in the mixing system. The performance of the algorithm is studied in simulations.

关键词： Additive noise Blind source separation Covariance matrix Filters sensor arrays signal processing signal processing algorithms Source separation Speech enhancement Time varying systems

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Non-homogeneity detector for Gaussian and non-Gaussian interference scenarios

Non-homogeneity detector for Gaussian and non-Gaussian inter...

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2nd ieee sensor array and multichannel signal processing workshop

作者： Rangaswamy, M USAF Res Lab SNHE Hanscom AFB MA 01730 USA

This paper presents a statistical analysis of two schemes for intelligent STAP training data selection in Gaussian and non-Gaussian interference backgrounds. Performance analysis of the two methods is carried out usin... 详细信息

ISBN: (纸本)0780375513

关键词： Space time adaptive processing

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RIDGE REGRESSION AND KALMAN FILTERING FOR TARGET TRACKING IN WIRELESS sensor NETWORKS

RIDGE REGRESSION AND KALMAN FILTERING FOR TARGET TRACKING IN...

引用

8th ieee sensor array and multichannel signal processing workshop (sam)

作者： Mahfouz, Sandy Mourad-Chehade, Farah Honeine, Paul Farah, Joumana Snoussi, Hichem Univ Technol Troyes Inst Charles Delaunay CNRS Troyes France Univ St Esprit Kaslik Fac Engn Dept Telecommun Juniyah Lebanon

ISBN: (纸本)9781479914814

This paper introduces an original method for target tracking in wireless sensor networks that combines machine learning and Kalman filtering. A database of radio-fingerprints is used, along with the ridge regression learning method, to compute a model that takes as input RSSI information, and yields, as output, the positions where the RSSIs are measured. This model leads to a position estimate for each target. The Kalman filter is used afterwards to combine the model's estimates with predictions of the target's positions based on acceleration information, leading to more accurate ones.

关键词： radio-fingerprinting Kalman filter ridge regression RSSI tracking WSN

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Augmented L-shaped Nested array Based on the Fourth-order Difference Co-array Concept 10

Augmented L-shaped Nested Array Based on the Fourth-order Di...

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10th ieee sensor array and multichannel signal processing workshop (sam)

作者： Li, Xiangnan Ren, Shiwei Liu, Jianyan Wang, Weijiang Beijing Inst Technol Sch Informat & Elect Beijing 100081 Peoples R China Sci & Technol Space Phys Lab Beijing 100076 Peoples R China

ISBN: (纸本)9781538647523

This paper presents a new two dimentional(2D) sparse array called the augmented L-shaped nested array based on the concept of fourth-order difference co-array. By properly choosing the relative positions of two L-shaped nested arrays, one could obtain a virtual planar array containing a large-scale hole-free uniform rectangular co-array. As a result, the degree of freedom could be greatly enhanced. Many theoretical properties are derived and simulations are included by considering the fourth-order cumulants of quasi-stationary signal to validate the superior performance of the proposed array in comparison with other 2D sparse arrays.

关键词： Two dimensional displays sensor arrays Covariance matrices array signal processing Planar arrays Conferences"

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Impact of Space-Time Covariance Estimation Errors on a Parahermitian Matrix EVD 10

Impact of Space-Time Covariance Estimation Errors on a Parah...

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10th ieee sensor array and multichannel signal processing workshop (sam)

作者： Delaosa, Connor Coutts, Fraser K. Pestana, Jennifer Weiss, Stephan Univ Strathclyde Dept EEE Glasgow Lanark Scotland Univ Strathclyde Dept Maths & Stats Glasgow Lanark Scotland

ISBN: (纸本)9781538647523

This paper studies the impact of estimation errors in the sample space-time covariance matrix on its parahermitian matrix eigenvalue decomposition. We provide theoretical bounds for the perturbation of the ground-truth eigenvalues and of the subspaces of their corresponding eigenvectors. We show that for the eigenvalues, the perturbation depends on the norm of the estimation error in the space-time covariance matrix, while the perturbation of eigenvector subspaces can additionally be influenced by the distance between the eigenvalues. We confirm these theoretical results by simulations.

关键词： broadband array processing space-time covariance estimation parahermitian matrix eigenvalue decomposition

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