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

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Near-field scatter measurements using quadratic sensor-angle distributions

Near-field scatter measurements using quadratic sensor-angle...

引用

2nd ieee sensor array and multichannel signal processing workshop

作者： Frazer, GJ Amin, MG DSTO ISR Div Edinburgh Australia

ISBN: (纸本)0780375513

The sensor-Angle Distribution (SAD) is a recently introduced tool representing the power arriving at each sensor as a function of angle (or spatial frequency). It can be used to characterize near-field scatter environments. The SAD, as originally introduced, undersampled the spatial correlation of the received signal (measured at each sensor) causing the SAD to be aliased for common source location cases. In this paper we indicate how this may be overcome. Additional results are provided showing that the SAD may be implemented as a multiple weighted subarray beamformer.

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SPARSE BAYESIAN LEARNING FOR DOA ESTIMATION USING CO-PRIME AND NESTED arrayS 10

SPARSE BAYESIAN LEARNING FOR DOA ESTIMATION USING CO-PRIME A...

引用

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

作者： Nannuru, Santosh Gerstoft, Peter Koochakzadeh, Ali Pal, Piya Univ Calif San Diego Scripps Inst Oceanog La Jolla CA 92093 USA Univ Calif San Diego Elect & Comp Engn La Jolla CA 92093 USA

ISBN: (纸本)9781538647523

Sparse Bayesian learning (SBL) has been used to obtain source direction-of-arrivals (DoAs) from uniform linear array (ULA) data. The maximum number of sources that can be resolved using a ULA is limited by the number of sensors in the array. It is known that sparse linear arrays such as co-prime and nested arrays can resolve more sources than the number of sensors. In this paper we demonstrate this using SBL. We compute the mean squared error in source power estimation as various parameters are varied.

关键词： Sparse Bayesian learning co-prime array nested array DoA estimation compressed sensing

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On time-invariant FDA beam-pattern design based on time-dependent frequency offsets 11

On time-invariant FDA beam-pattern design based on time-depe...

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

作者： Shi, Jiantao Nanjing Res Inst Elect Technol Nanjing 210039 Peoples R China CETC Key Lab Intellisense Technol Nanjing 210039 Peoples R China

ISBN: (纸本)9781728119465

In the existing works on FDA beam-patterns produced by the traditional FDA are time variant, and this causes difficulties in target detecting and beam-pattern steering. For solving the problem, several time-dependent frequency offsets based time-invariant FDA beam-pattern design methods have been proposed, which, however, neglected the time-range relationship and led to erroneous conclusions. In this note, considering the practical constraint and the relationship between time and range, it is concluded that FDA beam-patterns are always time-variant and it is unable to form time-invariant beam-patterns.

关键词： Frequency diverse array time-dependent frequency offset time-invariant beam-pattern time-range relationship

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Direction of Arrival Estimation with Microphone arrays Using SRP-PHAT and Neural Networks 10

Direction of Arrival Estimation with Microphone Arrays Using...

引用

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

作者： Diaz-Guerra, David Beltran, Jose R. Univ Zaragoza Dept Elect Engn & Commun Zaragoza Spain

ISBN: (纸本)9781538647523

The Steered Response Power with phase transform (SRP-PHAT) is one of the most employed techniques for Direction of Arrival (DOA) estimation with microphone arrays due its robustness against acoustical conditions as reverberation or noise. Among its main drawbacks is the growth of its computational complexity when the search space increases. To solve this issue, we propose the use of Neural Networks (NN) to obtain the DOA as a regression problem from a low resolution SRP-PHAT power map. The NNs can learn and exploit the information of the acoustic reflections of the room where the array is located with a training method that can be easily performed by an end user without technical knowledge.

关键词： microphone array direction of arrival DOA Sound Source Localization SSL SRP SRP-PHAT neural netowrks tele-conference system Wireless Acoustic sensor Network WASN WSN

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A Moving Horizon Convex Relaxation for Mobile sensor Network Localization

A Moving Horizon Convex Relaxation for Mobile Sensor Network...

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

作者： Simonetto, Andrea Leus, Geert Delft Univ Technol Fac EEMCS NL-2826 CD Delft Netherlands

ISBN: (纸本)9781479914814

In mobile sensor network localization problems we seek to estimate the position of the mobile sensor nodes by using a subset of pair-wise range measurements (among the nodes and with mobile anchors). When the sensor nodes are static, convex relaxations have been shown to provide a remarkably accurate approximate solution to this NP-hard estimation problem. In this paper, we propose a novel convex relaxation to tackle the more challenging dynamic case and we develop a moving horizon convex estimator based on a maximum a posteriori (MAP) formulation. The resulting estimator is then compared to standard extended and unscented Kalman filters both with respect to computational complexity and performance with simulated data. The results are promising, yet a more detailed analysis is needed.

关键词： sensor networks Convexity Mobile Ganglia Kalman filters Horizon complexity classes Moving Relaxation

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Toeplitz Structured Covariance Matrix Estimation for Radar Applications 11

Toeplitz Structured Covariance Matrix Estimation for Radar A...

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

作者： Du, Xiaolin Aubry, Augusto De Maio, Antonio Cui, Guolong Univ Elect Sci & Technol China Sch Informat & Commun Engn Chengdu Peoples R China Yantai Univ Sch Comp & Control Engn Yantai Peoples R China Univ Napoli Federico II DIETI Naples Italy

ISBN: (纸本)9781728119465

Following a geometric paradigm, the estimation of a Toeplitz structured covariance matrix is considered. The estimator minimizes the distance from the sample Covariance Matrix (SCM) while complying with some specific constraints modeling the covariance structure. The resulting constrained optimization problem is solved globally resorting to the Dykstra' projection framework. Each step of the procedure involves the solution of two convex sub-problems, whose minimizers are available in closed form. Simulation results related to typical radar environments highlight the effectiveness of the devised method.

关键词： Radar signal processing covariance matrix estimation projection Toeplitz structure

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A low complexity 2-D adaptive array for separating uncorrelated sources

A low complexity 2-D adaptive array for separating uncorrela...

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

作者： Ko, CC Natl Univ Singapore Dept Elect Engn Singapore 117548 Singapore

ISBN: (纸本)0780363396

This paper describes a 2-D adaptive array for separating uncorrelated sources. A matrix preprocessing beamformer, which will result in outputs from individual sources in the steady state, is used. The beamformer is updated periodically using the most recently available source location estimates. These estimates are in turn refined by using the beamformer outputs to adaptively eliminate correlated components in a reference element of the array. Although its initial convergence may be rather erratic, the adaptive algorithm has low O(M) complexity and has only 1 asymptotic time constant.

关键词： Adaptive algorithm Adaptive arrays array signal processing Attenuation Convergence Noise generators Position measurement Power generation Steady-state Working environment noise

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2-D sensor position perturbation analysis: Equivalence to AWGN on array outputs

2-D sensor position perturbation analysis: Equivalence to AW...

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2nd ieee sensor array and multichannel signal processing workshop

作者： Cevher, V McClellan, JH Georgia Inst Technol Atlanta GA 30332 USA

ISBN: (纸本)0780375513

In this paper, the performance of a subspace beamformer, namely the multiple signal classification algorithm (MUSIC), is scrutinized in the presence of sensor position errors. Based on a perturbation model, a relationship between the array autocorrelation matrix and the source autocorrelation matrix is established. It is shown that under certain assumptions on the source signals, the Gaussian sensor perturbation errors can be modelled as additive white Gaussian noise (AWGN) for an array where sensor positions are known perfectly. This correspondence can be used to equate position errors to an equivalent signal-to-noise ratio (SNR) for AWGN in performance evaluation. Finally, Cramer-Rao bound for the position perturbations that can be computed using the Cramer-Rao bound relations for the additive Gaussian noise case at high SNR's.

关键词： signal to noise ratio

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Iterative Sparse Through-the-Wall Imaging

Iterative Sparse Through-the-Wall Imaging

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ieee 7th sensor array and multichannel signal processing workshop (sam)

作者： Nikolic, Marija Nehorai, Arye Djordjevic, Antonije Univ Belgrade Sch Elect Engn Bulevar Kralja Aleksandra 73 Belgrade 11000 Serbia Washington Univ Dept Elect & Syst Engn St Louis MO 63130 USA

ISBN: (纸本)9781467310710

We consider iterative electromagnetic imaging of metallic targets hidden behind dielectric walls using sparse regularization. As in the equivalent-source method, we assume that the targets can be approximated by a set of filament currents whose locations are unknown. By using the iterative procedure, we reveal additional filament currents thus enlarging the target image. In order to take into account the mutual interaction between the current sources, and yet obtain the model amenable to signal processing, we combine the method of moments and the ray tracing.

关键词： target image Ray tracing Current sources Iterative Iterative Process METHOD OF MOMENTS Cytoskeletal Filaments signal processing Imaging

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Exploiting array Motion for Augmentation of Co-prime arrays

Exploiting Array Motion for Augmentation of Co-prime Arrays

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

作者： Ramirez, Juan, Jr. Odom, Jonathan Krolik, Jeffrey Duke Univ Dept Elect Comp Engn Durham NC 27708 USA

ISBN: (纸本)9781479914814

In this paper, we combine concepts from synthetic aperture processing and non-uniform linear array theory. Our objective is to use array motion to synthesize an array that can achieve the performance of a filled uniform linear array (ULA), but with fewer sensors than required for spatial Nyquist sampling. The class of physical arrays we use for synthesis are co-prime arrays constructed by nesting under-sampled ULA's with co-prime inter-element spacings. In particular, we use array motion to fill in missing co-array spacings. For co-prime M and N, a physical array of M + 2 N - 1 sensors plus modest array motion is used to achieve a filled co-array corresponding to approximately 2 M N elements. This facilitates spatial spectral estimation via non-adaptive beamforming over an extended aperture with low sidelobe performance.

关键词： Spectrum analysis

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