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Conference on Automatic Target Recognition XXV

作者： Alkhweldi, Marwan Cao, Zhicheng Schmid, Natalia A. W Virginia Univ Lane Dept Comp Sci & Elect Engn Morgantown WV 26506 USA

ISBN: (纸本)9781628415926

This paper considers a problem of distributed function estimation in the case when sensor locations are modeled as Gaussian random variables. We consider a scenario where sensors are deployed in clusters with cluster centers known a priori (or estimated by a high performance GPS) and the average quadratic spread of sensors around the cluster center also known. distributed sensors make noisy observations about an unknown parametric field generated by a physical object of interest (for example, magnetic field generated by a ferrous object and sensed by a network of magnetometers). Each sensor then performs local signal processing of its noisy observation and sends it to a central processor (called fusion center) in the wireless sensor network over parallel channels corrupted by fading and additive noise. The central processor combines the set of received signals to form an estimate of the unknown parametric field. In our numerical analysis, we involve a field shaped as a Gaussian bell. We experiment with the size of sensor clusters and with their number. A mean square error between the estimated parameters of the field and the true parameters used in simulations is involved as a performance measure. It can be shown that a relatively good estimate of the field can be obtained with only a small number of clusters. As the number of clusters increases, the estimation performance steadily improves. The results also indicate that, on the average, the number of clusters has more impact on the performance than the number of sensors per cluster, given the same size of the total network.

关键词： wireless sensor network maximum-likelihood estimation sensor localization random sensor placement distributed parameter estimation

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A Mixed Time-Scale Algorithm for distributed parameter estimation : NonLinear Observation Models and Imperfect Communication

A Mixed Time-Scale Algorithm for Distributed Parameter Estim...

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IEEE International Conference on Acoustics, Speech and Signal Processing

作者： Kar, Soummya Moura, Jose A. F. Carnegie Mellon Univ Dept Elect & Comp Engn Pittsburgh PA 15213 USA

ISBN: (纸本)9781424423538

The paper considers the algorithm NLU for distributed (vector) parameter estimation in sensor networks, where, the local observation models are nonlinear, and inter-sensor communication is imperfect, in the sense, that the network links fail randomly and inter-sensor transmission is quantized. The paper introduces the class of separably estimable observation models, which generalizes the notion of observability in centralized linear estimation to distributed nonlinear estimation. We show that the NLU algorithm leads to consistent and asymptotically unbiased estimates of the parameter at each sensor for separably estimable observation models. In other words, the sensors reach consensus almost sure (a.s.) to the true parameter value. The algorithm NLU is a mixed time scale stochastic algorithm, characterized by two different decreasing weight sequences associated with the consensus and innovation updates. The analysis of the NLU algorithm, thus, does not follow under the purview of standard stochastic approximation, making the analysis developed in the paper of independent theoretical interest.

关键词： distributed parameter estimation separably estimable stochastic approximation Laplacian consenus

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distributed estimation of a Parametric Field: Algorithms and Performance Analysis

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IEEE TRANSACTIONS ON SIGNAL PROCESSING 2014年第5期62卷 1041-1053页

作者： Talarico, Salvatore Schmid, Natalia A. Alkhweldi, Marwan Valenti, Matthew C. W Virginia Univ Dept Comp Sci & Elect Engn Morgantown WV 26506 USA

This paper presents a distributed estimator for a deterministic parametric physical field sensed by a homogeneous sensor network and develops a new transformed expression for the Cramer-Rao lower bound (CRLB) on the variance of distributed estimates. Stochastic models used in this paper assume additive noise in both the observation and transmission channels. Two cases of data transmission are considered. The first case assumes a linear analog modulation of raw observations prior to their transmission to a fusion center. In the second case, each sensor quantizes its observation to M levels, and the quantized data are communicated to a fusion center. In both cases, parallel additive white Gaussian channels are assumed. The paper develops an iterative expectation-maximization (EM) algorithm to estimate unknown parameters of a parametric field, and its linearized version is adopted for numerical analysis. The performance of the developed numerical solution is compared to the performance of a simple iterative approach based on Newton's approximation. Numerical examples are provided for the case of a field modeled as a Gaussian bell. However, the distributed estimator and the derived CRLB are general and can be applied to any parametric field. The dependence of the mean-square error (MSE) on the number of quantization levels, the number of sensors in the network and the SNR of the observation and transmission channels are analyzed. The variance of the estimates is compared to the derived CRLB.

关键词： Cramer-Rao lower bound distributed parameter estimation EM algorithm maximum-likelihood estimation wireless sensor network

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Optimal Observations Transmission for distributed estimation under Energy Constraint

Optimal Observations Transmission for Distributed Estimation...

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IEEE Symposium on Computational Intelligence for Communication Systems and Networks (CIComms)

作者： Alkhweldi, Marwan West Virginia Univ Lane Dept Comp Sci & Elect Engn Morgantown WV 26506 USA

ISBN: (纸本)9781479945245

This paper studies the problem of distributed parameter estimation in wireless sensor network under energy constraints. Optimization formulas that find the optimal sensors' observations transmission that guarantee the best estimation performance from the available energy are derived. The network consists of sensors that are deployed over an area at random. Sensors' observations are noisy measurements of an underlying field. Sensors have limited energy for the transmission process. Each sensor processes its observation prior to transmitting it to a fusion center, where a field parameter vector is estimated. Transmission channels between the sensors and the fusion center are assumed to be noisy parallel channels. The sensors' locations, the noise probability density function, and the field characteristic function are assumed to be known at the fusion center. Simulation results which support the optimization formulas are shown.

关键词： Wireless sensor network maximum-likelihood estimation distributed parameter estimation mean squared error energy constraint

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Diagnosis of inhomogeneous insulation degradation in electric cables by distributed shunt conductance estimation

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CONTROL ENGINEERING PRACTICE 2013年第9期21卷 1195-1203页

作者： Zhang, Qinghua Tang, Huaibin INRIA F-35042 Rennes France Shandong Univ Sch Informat Sci & Engn Jinan 250100 Peoples R China

For the diagnosis of inhomogeneous insulation degradation in electric cables, the estimation of distributed shunt conductance is studied in this paper. Gradual growth of the shunt conductance is a consequence of degradation of the dielectric properties of the insulator. The proposed estimation method is based on voltage and current measurements at a single end of the cable. After the linearization of the bilinear term of the telegrapher's equations through a perturbation approach, the Kalman filter is applied to transform the problem of dynamic system parameter estimation to a simple linear regression problem. Results of numerical simulations are presented to demonstrate the feasibility of the proposed method. In particular, it is shown that the weak sensitivity of the available measurements to the shunt conductance can be compensated by long time data samples. (C) 2013 Elsevier Ltd. All rights reserved.

关键词： Fault diagnosis distributed parameter estimation Electric cables Telegrapher's equations

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distributed Linear parameter estimation in Sensor Networks-Convergence Properties

Distributed Linear Parameter Estimation in Sensor Networks-C...

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42nd Asilomar Conference on Signals, Systems and Computers

作者： Kar, Soummya Moura, Jose M. F. Carnegie Mellon Univ Dept Elect & Comp Engn Pittsburgh PA 15213 USA

ISBN: (纸本)9781424429400

The paper considers the problem of distributed linear vector parameter estimation in sensor networks, when sensors can exchange quantized state information and the inter-sensor communication links fail randomly. We show that our algorithm LU leads to almost sure (a.s.) consensus of the local sensor estimates to the true parameter value, under the assumptions that, a minimal global observability criterion is satisfied and the network is connected in the mean, i.e., lambda(2)((L) over bar) > 0, where (L) over bar is the expected Laplacian matrix. We show that the local sensor estimates are asymptotically normal and characterize the convergence rate of the algorithm in the framework of moderate deviations.

关键词： distributed parameter estimation Random Link Failures Quantization Consistency Asymptotic Normality

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Identification of spatiotemporally invariant systems for control adaptation

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AUTOMATICA 2012年第9期48卷 2079-2092页

作者： Sarwar, Azeem Voulgaris, Petros G. Salapaka, Srinivasa M. Univ Maryland Fischell Dept Bioengn College Pk MD 20742 USA Univ Illinois Dept Aerosp Engn Coordinated Sci Lab Urbana IL USA Univ Illinois Dept Elect & Comp Engn Urbana IL USA

We present a distributed projection algorithm for system identification of spatiotemporally invariant systems with the ultimate purpose of utilizing it in an indirect adaptive control scheme. Each subsystem communicates only with its immediate neighbors to share its current estimate along with a cumulative improvement index. On the basis of the cumulative improvement index, the best estimate available is picked in order to carry out the next iteration. For small estimation error, the scheme switches over to a "smart" averaging routine. The proposed algorithm guarantees to bring the local estimates arbitrarily close to one another, developing a "local consensus", which makes it amenable to control by the application of indirect distributed adaptive control schemes. It is also shown through simulations that the proposed algorithm has a clear advantage over the standard projection algorithm. Our proposed algorithm is also suitable for addressing the estimation problem in distributed networks that arise in a variety of applications, such as environment monitoring, target localization and potential sensor network problems. Published by Elsevier Ltd

关键词： Spatially invariant Spatiotemporal systems Identification Interconnected systems Local consensus Complex systems distributed parameter estimation

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PREDICTIVE STATE VECTOR ENCODING FOR DECENTRALIZED FIELD estimation IN SENSOR NETWORKS

PREDICTIVE STATE VECTOR ENCODING FOR DECENTRALIZED FIELD EST...

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IEEE International Conference on Acoustics, Speech and Signal Processing

作者： Xaver, Florian Matz, Gerald Gerstoft, Peter Mecklenbraeuker, Christoph Vienna Univ Technol Inst Telecommun A-1040 Vienna Austria Univ Calif San Diego Scripps Inst Oceanog Marine Phys Lab La Jolla CA 92093 USA

ISBN: (纸本)9781467300469

Decentralized physics-based field estimation in clustered sensor networks requires the exchange of state vectors between neighboring clusters. We reduce the communication overhead between clusters by using a differential encoding of state vectors that exploits the spatio-temporal field dependencies. This encoding involves a Kalman prediction step that builds on the state-space equations governing the field's spatio-temporal evolution. The Kalman step keeps the computational complexity low. Simulation results for an acoustic field demonstrate the approach.

关键词： distributed parameter estimation Kalman filter differential encoding linear prediction acoustic field

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Diagnosis of inhomogeneous insulation degradation in electric cables by distributed shunt conductance estimation *

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IFAC Proceedings Volumes 2012年第16期45卷 607-612页

作者： Qinghua Zhang Huaibin Tang INRIA Campus de Beaulieu 35042 Rennes Cedex France School of Information Science and Engineering Shandong University 250100 Jinan P. R. China

For the diagnosis of inhomogeneous insulation degradation in electrical cables, the estimation of the distributed shunt conductance is studied in this paper. Gradual growth of the shunt conductance is a consequence of degradation of the dielectric properties of the insulator. The proposed estimation method is based on voltage and current measurements at a single end of the cable. After the linearization of the bilinear term of the telegrapher's equations through a perturbation approach, the Kalman filter is applied to transform the problem of dynamic system parameter estimation to a simple linear regression problem. Results of numerical simulations are presented to demonstrate the feasibility of the proposed method. In particular, it is shown that the weak sensitivity of the available measurements to the shunt conductance can be compensated by long time data samples.

关键词： Fault diagnosis distributed parameter estimation electric cables telegrapher's equations

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A semi-implicit approach for fast parameter estimation by means of the extended Kalman filter

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JOURNAL OF PROCESS CONTROL 2011年第4期21卷 510-518页

作者： Gryzlov, A. Leskens, M. Mudde, R. F. Delft Univ Technol Dept Multiscale Phys NL-2628 BW Delft Zuid Holland Netherlands Delft Univ Technol Delft Ctr Syst & Control NL-2628 BW Delft Zuid Holland Netherlands

An assessment is performed of the impact of the time-integration scheme on data assimilation based on the extended Kalman filter approach. The usage of the implicit Euler scheme, which leads to unconditionally stable model updates for the whole range of time steps, results in a less accurate estimation of model parameters, which can be overcome using a parameter estimator based on the explicit Euler scheme. However, the latter limits the maximum time step which may lead to an unacceptable increase in computational load. The alternative can be found using a semi-implicit estimator, where a model is updated using the implicit Euler scheme, whereas the propagation of the error covariance matrix in time is based on the explicit time integration scheme. The improved properties of the proposed algorithm are demonstrated on a one-dimensional problem of permeability estimation in a porous medium for a single phase oil flow. (C) 2011 Elsevier Ltd. All rights reserved.

关键词： distributed parameter estimation Extended Kalman filter Numerical scheme Inverse dynamic problem

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