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Augmented L-Shaped Nested array Based on the Fourth-Order Difference Co-array Concept

Augmented L-Shaped Nested Array Based on the Fourth-Order Di...

引用

sensor array and multichannel signal processing workshop

作者： Xiangnan Li Shiwei Ren Jianyan Liu Weijiang Wang School of Information and Electronics Beijing Institute of Technology Beijing China Science and Technology on Space Physics Laboratory Beijing China

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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DOA Estimation of Quasi-Stationary signals with a Nested array in Unknown Noise Field

DOA Estimation of Quasi-Stationary Signals with a Nested Arr...

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sensor array and multichannel signal processing workshop

作者： Huiping Huang Bin Liao Qiuxiang Shen College of Information Engineering Shenzhen University Shenzhen Guangdong Province China

The problem of direction-of-arrival (DOA) estimation of quasi-stationary signals using a nested array in unknown noise field is investigated and two methods based on differencing method and eigenspace are proposed in this paper. Through performing differencing between the virtual received data, the unknown noise component is directly removed from the data model. The resulting data model is then utilized to find out the DOAs of the signals based on the theory of subspace. Thanks to the configuration of the nested array, it can produce large degrees of freedom (DOF) in the virtual sensor array which provides the virtual received data. These increased DOF can contribute enhanced resolution in DOA estimation. Numerical simulations of DOA estimation exhibit the effectiveness and superiority of the proposed methods.

关键词： sensor arrays Direction-of-arrival estimation Multiple signal classification Estimation array signal processing Data models

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Model Order Determination for Signed Measurements via the Bayesian Information Criterion

Model Order Determination for Signed Measurements via the Ba...

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sensor array and multichannel signal processing workshop

作者： Changheng Li Rong Zhang Jian Li Petre Stoica Department of EEIS University of Science and Technology of China Hefei Anhui P.R. china Department of ECE University of Florida Gainesville Florida USA Department of Information Technology Uppsala University Uppsala Sweden

The problem of model order selection from signed measurements obtained via L-blt sampling is considered. The extension of the Bayesian information criterion (BIC) to the case of 1-blt sampling, referred to as 1bBIC, is proposed to determine the signal model order. Numerical examples are presented to demonstrate the performance of 1bBIC when the One-Bit RELAX algorithm is used to estimate the parameters of sinusoidal signals.

关键词： signal to noise ratio Numerical models Bayes methods Maximum likelihood estimation array signal processing sensor arrays

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Localization of Multiple Simultaneously Active Speakers in an Acoustic sensor Network

Localization of Multiple Simultaneously Active Speakers in a...

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sensor array and multichannel signal processing workshop

作者： Andreas Brendel Sharon Gannot Walter Kellermann Multimedia Communications and Signal Processing Friedrich-Alexander-Universität Erlangen-Nürnberg Erlangen Germany Faculty of Engineering Bar-Ilan University Ramat-Gan Israel

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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Estimation of the Regularization Parameter of an On-Line NMF with Minimum Volume Constraint

Estimation of the Regularization Parameter of an On-Line NMF...

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sensor array and multichannel signal processing workshop

作者： Ludivine Nus Sebastian Miron David Brie CRAN Université de Lorraine France

In this paper, the estimation of the regularization parameter of the on-line Non-negative Matrix Factorization (NMF) with minimum volume constraint on sources is addressed. Adding a volume constraint in the model is important to ensure uniqueness of the solution and good data representation. However, the effectiveness of this approach is hampered by the optimal determination of the strength of minimum volume term. To solve this problem, we formulate it as a bi-objective optimization problem and three Minimum Distance Criterion (MDC) strategies are proposed and evaluated. The three strategies yield similar results but one of them in particular yields an interesting tradeoff between accuracy and computation time.

关键词： Hyperspectral imaging signal processing algorithms Noise measurement array signal processing sensor arrays

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

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

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sensor array and multichannel signal processing workshop

作者： Connor Delaosa Fraser K. Coutts Jennifer Pestana Stephan Weiss Dept. of EEE University of Strathclyde Dept. of Maths & Stats University of Strathclyde

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： Eigenvalues and eigenfunctions Covariance matrices Estimation error Matrix decomposition Perturbation methods array signal processing

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An Event-Based Diffusion LMS Strategy

An Event-Based Diffusion LMS Strategy

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sensor array and multichannel signal processing workshop

作者： Yuan Wang Wee Peng Tay Wuhua Hu School of Electrical and Electronic Engineering Nanyang Technological University Singapore Department of Artificial Intelligence for Applications SF Technology Co. Ltd Shenzhen China

We consider an event-based communication mechanism for diffusion least mean-squares estimation in a sensor network, in which an intermediate estimate from a sensor is communicated to its neighbors only when a triggering criterion is satisfied. We provide a sufficient condition for the mean error stability of our proposed event-based diffusion strategy, and derive an upper bound of its steady-state network mean-square deviation (MSD). Simulations demonstrate that our event-based strategy can achieve similar steady-state network MSD as the adapt-then-combine diffusion strategy but at a significantly lower communication rate.

关键词： Steady-state Upper bound array signal processing sensor arrays Conferences Stability analysis

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A Performance-Weighted Blended Dominant Mode Rejection Beamformer

A Performance-Weighted Blended Dominant Mode Rejection Beamf...

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sensor array and multichannel signal processing workshop

作者： John R. Buck Andrew C. Singer ECE Department University of Massachusetts Dartmouth Dartmouth MA USA ECE Department University of Illinois Urbana-Champaign Urbana IL USA

Adaptive beamformers operating in snapshot deficient situations often estimate regularization parameters such as the diagonal loading level or the signal subspace dimension. We propose a new beamformer that avoids this problem by computing its array weights as a mixture of the array weights for a set of beamformers. The new beamformer's average output power asymptotically approaches the best performance of any of the beamformers in the set. Applying this technique to the Dominant Mode Rejection (DMR) beamformer obviates the need to estimate the dominant signal subspace dimension. An example simulation of a complicated passive sonar scenario illustrates that the blended beamformer's performance rivals the performance of the best fixed subspace DMR beamformer, and may outperform all of them in nonstationary environments.

关键词： Power generation sensor arrays array signal processing Direction-of-arrival estimation Adaptation models Loading

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Adaptive Near-Field Imaging with Robotic arrays

Adaptive Near-Field Imaging with Robotic Arrays

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sensor array and multichannel signal processing workshop

作者： Belal Korany Chitra R. Karanam Yasamin Mostofi University of California Santa Barbara Santa Barbara CA USA

In this paper, we are interested in phased-array imaging of an unknown area using narrowband RF signals and arrays synthesized by an unmanned vehicle. Typical phased array imaging approaches use fixed or pre-determined array configurations for imaging, which are not usually informative for the whole area. In this paper, we then propose an iterative adaptive imaging approach where we identify the uncertain regions in an initial image that need to be sensed better, find the optimal array location and orientation for such a sensing task and finally combine the new image with the initial image to obtain a better overall imaging quality. Such an approach is easily enabled by using robots to synthesize arrays. We validate our proposed approach with numerical simulations and show that an adaptive approach improves the imaging quality as compared to imaging with fixed arrays.

关键词： Imaging Robot sensing systems array signal processing Uncertainty Adaptive arrays sensor arrays

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Computationally Efficient Waveform Design in Spectrally Dense Environment

Computationally Efficient Waveform Design in Spectrally Dens...

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sensor array and multichannel signal processing workshop

作者： Markus Yli-Niemi Sergiy A. Vorobyov Dept. Signal Processing and Acoustics Aalto University Aalto Finland

The problem of unimodular radar waveform design with similarity constraint in spectrally dense environment is considered. The corresponding optimization problem is nonconvex 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 nonconvex, 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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