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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...

引用

2016 ieee sensor array and multichannel signal processing workshop, SAM 2016

作者： Elbir, Ahmet M. Engin Tuncer, T. Dept. of Electrical and Electronics Engineering Middle East Technical University Ankara06800 Turkey

ISBN: (纸本)9781509021031

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 ajoint-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. © 2016 ieee.

关键词： Compressed sensing

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Spatial spectral estimation using a coprime sensor array with the min processor

Spatial spectral estimation using a coprime sensor array wit...

引用

2016 ieee sensor array and multichannel signal processing workshop, SAM 2016

作者： Liu, Yang Buck, John R. Electrical and Computer Engineering Department University of Massachusetts Dartmouth DartmouthMA United States

ISBN: (纸本)9781509021031

A coprime sensor array (CSA) estimates the spatial power spectral density (PSD) of the observed signal by multiplying one conventionally beamformed subarray scanned response with the complex conjugate of the other. This product processor removes the CSA subarray spatial aliasing ambiguities, but has a peak sidelobe higher than the peak sidelobe of a fully populated uniform linear array (ULA). Moreover, the resulting PSD estimate is not necessarily positive semi-definite. This paper proposes choosing the minimum of the two CSA subarray scanned responses at each bearing to resolve the spatial aliasing ambiguities. The min processor achieves lower peak sidelobe height and total sidelobe area than the product processor and preserves the positive semi-definite property of the PSD. This paper presents closed form expressions for the first two moments of the CSA min PSD estimator. Simulation results show the min processor achieves a lower PSD estimate variance than the product processor while maintaining the same array resolution. © 2016 ieee.

关键词： Power spectral density

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A Monte Carlo scheme for node-specific inference over wireless sensor networks

A Monte Carlo scheme for node-specific inference over wirele...

引用

2016 ieee sensor array and multichannel signal processing workshop, SAM 2016

作者： Martino, Luca Plata-Chaves, Jorge Louzada, Francisco Brazil Department of Electrical Engineering-ESAT STADIUS KU Leuven LeuvenB-3001 Belgium

ISBN: (纸本)9781509021031

In this work, we design an efficient Monte Carlo scheme for a node-specific inference problem where a vector of global parameters and multiple vectors of local parameters are involved. This scenario often appears in inference problems over heterogeneous wireless sensor networks where each node performs observations dependent on a vector of global parameters as well as a vector of local parameters. The proposed scheme uses parallel local MCMC chains and then an importance sampling (IS) fusion step that leverages all the observations of all the nodes when estimating the global parameters. The resulting algorithm is simple and flexible. It can be easily applied iteratively, or extended in a sequential framework. © 2016 ieee.

关键词： Vectors

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Performance improvement for wideband DOA estimation with white noise reduction based on uniform linear arrays

Performance improvement for wideband DOA estimation with whi...

引用

2016 ieee sensor array and multichannel signal processing workshop, SAM 2016

作者： Anbiyaei, Mohammad Reza Liu, Wei McLernon, Des C. Communications Research Group Department of Electronic and Electrical Engineering University of Sheffield SheffieldS1 3JD United Kingdom Signal Processing for Communications Group School of Electronic and Electrical Engineering University of Leeds LeedsLS2 9JT United Kingdom

ISBN: (纸本)9781509021031

A method is proposed for reducing the effect of white noise in wideband uniform linear arrays via a combination of a judiciously designed transformation followed by highpass filters. The reduced noise level leads to a higher signal to noise ratio for the system, which can have a significant effect on the performance of various direction of arrival (DOA) estimation methods. As a representative example, the compressive sensing-based wideband DOA estimation method is employed here to demonstrate the improved estimation performance, this is confirmed by simulation results. © 2016 ieee.

关键词： White noise

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Sparse antenna array design for directional modulation

Sparse antenna array design for directional modulation

引用

2016 ieee sensor array and multichannel signal processing workshop, SAM 2016

作者： Zhang, Bo Liu, Wei Gou, Xiaoming Communications Research Group Department of Electronic and Electrical Engineering University of Sheffield SheffieldS1 3JD United Kingdom School of Information and Electronics Beijing Institute of Technology Beijing100081 China

ISBN: (纸本)9781509021031

Directional modulation (DM) can be achieved based on uniform linear arrays (ULAs), where the maximum half wavelength spacing is needed to avoid spatial aliasing. To exploit the degrees of freedom (DOFs) in the spatial domain, sparse arrays can be employed for more effective DM design. In this paper, the problem of antenna location optimisation for sparse arrays in the context of DM is addressed for the first time, where compressive sensing based formulations are proposed employing the group sparsity concept. Design examples are provided to verify the effectiveness of the proposed designs. © 2016 ieee.

关键词： Compressed sensing

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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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2016 ieee sensor array and multichannel signal processing workshop, SAM 2016

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

ISBN: (纸本)9781509021031

Linearized Bregman iterations are low complexity and high precision approaches for solving the combined l1/l2 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 l1/l2 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 l1/l2 minimization can be performed with less complexity than the corresponding l2 minimization. © 2016 ieee.

关键词： Adaptive filters

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Point and beam-sparse radio astronomical source recovery using non-negative least squares

Point and beam-sparse radio astronomical source recovery usi...

引用

2016 ieee sensor array and multichannel signal processing workshop, SAM 2016

作者： Naghibzadeh, Shahrzad Sardarabadi, Ahmad Mouri Van Der Veen, Alle-Jan Facultuy of EEMCS Delft University of Technology Delft Netherlands Kapteyn Astronomical Institute University of Groningen Groningen Netherlands

ISBN: (纸本)9781509021031

A simple and novel algorithm for source recovery based on array data measurements in radio astronomy is proposed. Considering that a radioastronomical image is composed of both point sources and extended emissions, prior information on the images, namely non-negativity and substantial black background are taken into account to choose source representation basis functions. Dirac delta functions are chosen to represent point sources and a Gaussian function approximated from the main beam of the antenna array is selected to capture the extended emissions. We apply the non-negative least squares (NNLS) algorithm to estimate the basis coefficients. It is shown that the sparsity promoted by the NNLS algorithm based on the chosen basis functions results in a super-resolution (finer resolution than prescribed by the main beam of the antenna array pattern) estimate for the point sources and smooth recovery for the extended emissions. © 2016 ieee.

关键词： Image processing

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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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2016 ieee sensor array and multichannel signal processing workshop, SAM 2016

作者： Hassani, Amin Plata-Chaves, Jorge Bertrand, Alexander Moonen, Marc Department of Electrical Engineering-ESAT STADIUS KU Leuven LeuvenB-3001 Belgium

ISBN: (纸本)9781509021031

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. © 2016 ieee.

关键词： Direction of arrival

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Privacy-aware decentralized detection using linear precoding

Privacy-aware decentralized detection using linear precoding

引用

2016 ieee sensor array and multichannel signal processing workshop, SAM 2016

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

ISBN: (纸本)9781509021031

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. © 2016 ieee.

关键词： sensor networks

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A subspace method for array covariance matrix estimation

A subspace method for array covariance matrix estimation

引用

2016 ieee sensor array and multichannel signal processing workshop, SAM 2016

作者： Rahmani, Mostafa Atia, George K. Department of Electrical and Computer Engineering University of Central Florida OrlandoFL United States

ISBN: (纸本)9781509021031

This paper introduces a subspace method for the estimation of an array covariance matrix. When the received signals are uncorrelated, it is shown that the array covariance matrices lie in a special subspace defined through all possible correlation vectors of the received signals and whose dimension is typically much smaller than the ambient dimension. Based on this observation, a subspace-based covariance matrix estimator is proposed as a solution to a semi-definite convex optimization problem. While the optimization problem has no closed-form solution, a nearly optimal closed-form solution that is easily implementable is proposed. The proposed approach is shown to yield higher estimation accuracy than conventional approaches since it eliminates the estimation error that does not lie in the subspace of the true covariance matrices. The numerical examples demonstrate that the proposed estimator can significantly improve the estimation quality of the covariance matrix. © 2016 ieee.

关键词： Covariance matrix

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