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Collaborative Sparse signal Recovery In Hierarchical Wireless sensor Networks

Collaborative Sparse Signal Recovery In Hierarchical Wireles...

引用

3rd ieee International workshop on Computational Advances in Multi-sensor Adaptive processing (CAMSAP)

作者： Ling, Qing Tian, Zhi Michigan Technol Univ Dept Elect & Comp Engn Houghton MI 49931 USA

ISBN: (纸本)9781424451807

This paper investigates the design choices and implementation schemes for information fusion among cluster heads in a large-scale hierarchical wireless sensor network. Two main issues addressed are: whether to choose centralized processing with aid of a fusion center or decentralized collaboration among cluster heads, and for the latter choice, how to collaborate. Based on a sparse signal recovery problem arising from an environmental monitoring application, we propose a decentralized collaborative decision-making algorithm for cluster heads, and compare it with the centralized scheme. Our observation is: when the number of sensors within each cluster is quite large to induce a large amount of data, and the cluster heads are subject to multi-hop communications due to limited communication range, the collaborative algorithm is superior to the centralized one in terms of communication load and energy efficiency.

关键词： Decision making

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A Theoretical Framework for Problems Requiring Robust Behavior

A Theoretical Framework for Problems Requiring Robust Behavi...

引用

3rd ieee International workshop on Computational Advances in Multi-sensor Adaptive processing (CAMSAP)

作者： Carrillo, Rafael E. Aysal, Tuncer C. Barner, Kenneth E. Univ Delaware Dept Elect & Comp Engn Newark DE 19716 USA

ISBN: (纸本)9781424451807

This paper develops a generalized Cauchy density (GCD) based theoretical approach that allows the formulation of challenging problems in a robust fashion. The proposed framework subsumes the generalized Gaussian distribution (GGD) family based developments, thereby guaranteeing performance improvements over traditional problem formulation techniques. This robust framework can be adapted to a variety of applications in signal processing. We formulate two particular applications under this framework in this paper: 1) Robust reconstruction methods for compressed sensing and 2) robust estimation in sensor networks with noisy channels.

关键词： Maximum likelihood estimation nonlinear filtering and estimation impulsive noise compressed sensing sensor networks

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Robust Semiparametric Amplitude Estimation of Sinusoidal signals: The Multi-sensor Case

Robust Semiparametric Amplitude Estimation of Sinusoidal Sig...

引用

3rd ieee International workshop on Computational Advances in Multi-sensor Adaptive processing (CAMSAP)

作者： Muma, Michael Hammes, Ulrich Zoubir, Abdelhak M. Tech Univ Darmstadt Signal Proc Grp D-64283 Darmstadt Germany

ISBN: (纸本)9781424451807

The problem of robust estimation of the complex amplitudes of sinusoidal signals using multiple sensors, in an unknown heavy-tailed, spatially and temporally i.i.d. noise environement is considered. A semiparametric approach for this case is presented, where non-parametric estimation of the noise density is succeeded by maximum likelihood estimation incorporating the estimated density. The suggested approach adapts to the sensor measurements using a compact, and conceptually simple non-parametric transformation density estimation. Simulation results are provided, which illustrate the improvement of the presented approach over classical robust or non-robust estimation procedures, e.g. Huber's minimax estimator or the least-squares estimator.

关键词： Maximum likelihood estimation

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Pareto-Optimal Beamforming for the MISO Interference Channel With Partial CSI

Pareto-Optimal Beamforming for the MISO Interference Channel...

引用

3rd ieee International workshop on Computational Advances in Multi-sensor Adaptive processing (CAMSAP)

作者： Karipidis, Eleftherios Gruendinger, Andreas Lindblom, Johannes Larsson, Erik G. Linkoping Univ Commun Syst Div Dept Elect Engn ISY SE-58183 Linkoping Sweden Tech Univ Munich Assoc Inst Signal Processing Dept Elect Engn & Informat Technol D-80290 Munich Germany

ISBN: (纸本)9781424451791

We consider the problem of finding Pareto-optimal (PO) operating points for the multiple-input single-output (MISO) interference channel when the transmitters have statistical (covariance) channel knowledge. We devise a computationally efficient algorithm, based on semidefinite relaxation, to compute the PO rates and the enabling beamforming vectors. We illustrate the effectiveness of our algorithm by a numerical example.

关键词： Beamforming

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Hybrid Probabilistic Data Association and Variational Filtering for Multi-Target Tracking in Wireless sensor Networks

Hybrid Probabilistic Data Association and Variational Filter...

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3rd ieee International workshop on Computational Advances in Multi-sensor Adaptive processing (CAMSAP)

作者： Teng, Jing Snoussi, Hichem Richard, Cedric Zhou, Yi ICD/LM2S University of Technology of Troyes 10000 12 rue Marie Curie France

ISBN: (纸本)9781424451807

A hybrid signal processing scheme is proposed for distributed multi-target tracking (MTT). For the sake of resource efficiency in a wireless sensor network (WSN), we reduce the problem to parallel cluster-based single target tracking when the targets are far apart, and switch to MTT only when data association becomes ambiguous. A sequential monte carlo method is employed to assign the ambiguous observations to specific targets or clutter, based on association probabilities. Whereas the rest observations are incorporated by the variational filter, which approximates the distribution of involved particles by a simple Gaussian distribution for each target. The natural and adaptive message compression dramatically reduces the resource consumption of the WSN. The low computation complexity also guarantees the one-line execution of the hybrid MTT scheme. In addition, experimental results prove that the proposed scheme succeeds in distinguishing and tracking multiple targets even during the occlusions.

关键词： Wireless sensor networks

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Universal Priors for Sparse Modeling

Universal Priors for Sparse Modeling

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3rd ieee International workshop on Computational Advances in Multi-sensor Adaptive processing (CAMSAP)

作者： Ramirez, Ignacio Lecumberry, Federico Sapiro, Guillermo Univ Minnesota Dept Elect Engn 200 Union St SE Minneapolis MN 55455 USA Univ Republica Inst Ingn Electr Montevideo 11300 Uruguay

ISBN: (纸本)9781424451791

Sparse data models, where data is assumed to be well represented as a linear combination of a few elements from a dictionary, have gained considerable attention in recent years, and their use has led to state-of-the-art results in many signal and image processing tasks. It is now well understood that the choice of the sparsity regularization term is critical in the success of such models. In this work, we use tools from information theory to propose a sparsity regularization term which has several theoretical and practical advantages over the more standard l(0) or l(1) ones, and which leads to improved coding performance and accuracy in reconstruction tasks. We also briefly report on further improvements obtained by imposing low mutual coherence and Gram matrix norm on the learned dictionaries.

关键词： Image processing

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A SEMI-CLOSED-FORM SOLUTION TO OPTIMAL DECENTRALIZED BEAMFORMING FOR TWO-WAY RELAY NETWORKS

A SEMI-CLOSED-FORM SOLUTION TO OPTIMAL DECENTRALIZED BEAMFOR...

引用

3rd ieee International workshop on Computational Advances in Multi-sensor Adaptive processing (CAMSAP)

作者： Shahbazpanahi, Shahram Univ Ontario Fac Engn & Appl Sci Inst Technol Oshawa ON L1H 7K4 Canada

ISBN: (纸本)9781424451791

In this paper, we investigate the design of a distributed beamforming technique for two-way relay networks consisting of two transceivers and multiple relay nodes. We consider the problem of the total transmit power minimization subject to two constraints that maintain the transceivers' SNRs above given thresholds gamma(1) and gamma(2). For this problem, we obtain a semi-closed-form solution for the beamforming weight vector. Our solution is based on the observation that beamforming weight vector depends on the sum of SNR thresholds gamma(1) + gamma(2). As long as this sum does not change, the beamforming weight vector will not change. Hence, a two-way relay vector can be equivalently modeled as a one-way relay network where gamma(1) and gamma(2) are replaced with 0 and gamma(1) + gamma(2), respectively. Based on this equivalent one-way relaying scheme, the beamforming weight vector is obtained in a closed form if a certain intermediate parameter is given. This parameter is the transmit power of the source in the equivalent one-way relaying scheme and can be obtained efficiently using a simple bisection method. Once this parameter is obtained, the beamforming weight vector can be obtained in a closed form.

关键词： Distributed beamforming ad hoc relay networks two-way relaying distributed signal processing SNR balancing optimal power allocation bi-directional relaying

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OPTIMAL NETWORK SIZE AND ENCODING RATE FOR RANDOM FIELD ESTIMATION WITH WIRELESS sensor NETWORKS

OPTIMAL NETWORK SIZE AND ENCODING RATE FOR RANDOM FIELD ESTI...

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3rd ieee International workshop on Computational Advances in Multi-sensor Adaptive processing (CAMSAP)

作者： Matamoros, Javier Anton-Haro, Caries Ctr Tecnol Telecomunicac Catalunya Castelldefels 08860 Spain

ISBN: (纸本)9781424451791

In this paper, we address the problem of random field estimation with Wireless sensor Networks in the absence of CSI at the sensor nodes. We assume that sensors encode their observations at a common and constant rate which, on the one hand, determines the quantization noise at the encoders and, on the other, the outage probability experienced in the sensor-to-fusion center fading channels. These effects, arising from the power and bandwidth constraints that we impose, along with the spatial sampling process itself result into some distortion that we attempt to minimize by carefully selecting the optimal number of sensor nodes and the aforementioned encoding rate. On the basis of a (tight) upper bound, we derive closed-form expressions of the corresponding optimal values. Computer simulation results are mostly given in terms of optimal encoding rate and network size for different operating conditions (SNR, spatial variability of the random field) and reconstruction distortion vs. network size.

关键词： sensor nodes

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Distributed Projection Approximation Subspace Tracking Based on Consensus Propagation

Distributed Projection Approximation Subspace Tracking Based...

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3rd ieee International workshop on Computational Advances in Multi-sensor Adaptive processing (CAMSAP)

作者： Reyes, Carolina Hilaire, Thibault Mecklenbraeuker, Christoph F. Vienna Univ Technol Inst Commun & RF Engn A-1040 Vienna Austria Univ Paris 06 Lab Comp Sci LIP 6 Paris France Vienna Univ Technol Christian Doppler Lab Wireless Technol Sustainable Mobil A-1040 Vienna Austria

ISBN: (纸本)9781424451807

We develop and investigate a distributed algorithm for signal subspace tracking with a wireless sensor network without the need for a fusion center, in order to improve the robustness and scalability. We assume that all sensor nodes may broadcast messages to sensors in their neighborhood defined by a finite (small) communication radius. To this aim, we start from Projection Approximation Subspace Tracking (PAST) which is a well-investigated algorithm suitable for implementation in a fusion center. We arrive at a distributed approximation of the PAST algorithm by letting each sensor broadcast its local observation variable x(n) (t) and a filtered observation vector (y) under bar (n) (t) to its neighborhood. Vice versa, the received messages at sensor node n from its neighborhood are fused by employing consensus propagation. Finally, we investigate the proposed distributed algorithm in simulation runs.

关键词： sensor nodes

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Statistical Design of a 3D Microarray with Position-Encoded Microspheres

Statistical Design of a 3D Microarray with Position-Encoded ...

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3rd ieee International workshop on Computational Advances in Multi-sensor Adaptive processing (CAMSAP)

作者： Sarder, Pinaki Nehorai, Arye Washington Univ St Louis Dept Elect & Syst Engn St Louis MO 63130 USA

ISBN: (纸本)9781424451807

We propose a three-dimensional (3D) microarray device with fixed distance between the microspheres. The microspheres in the new device have controllable positions. To design the layout of our proposed device, we consider an optimal statistical performance analysis for imaging the microspheres. We compute the posterior Cramer-Rao bound on the error in estimating the unknown imaging parameters and use the bound to compute the minimal distance between the microspheres, which provides a desired level of statistical imaging performance. We illustrate our concept using a numerical example. The proposed microarray has high sensitivity, efficient packing density, and guaranteed imaging performance. It identifies targets based on the microspheres' locations, allowing significantly simplified imaging analysis.

关键词： Microspheres

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