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Estimation of sparse partial correlation graphs is discussed within the multivariate locally-stationary wavelet framework. We discuss the requirement for regularisation in such a framework and how this effects estimation. We observe that sparse model selection in the framework promotes more robust estimates of multivariate LSW processes, and improves interpretation through graph selection. The method is applied to study evolving correlation dynamics throughout an epileptic seizure.

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In this work, we propose a novel approach to multiple measurement vector (MMV) compressed sensing. We show that by exploiting the statistical properties of the sources, we can do better than previously derived lower bounds in this context. We show that in the MMV case, we can identify the active sources with fewer sensors than sources. We first develop a general framework for recovering the sparsity profile of the sources by combining ideas from compressed sensing with blind identification methods.

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5 Views

Cooperative localization plays a key role in location-aware service of wireless networks. However, the statistical-based estimator of network localization, e.g., the maximum likelihood estimator or the maximum a posterior estimator, is commonly non-convex due to nonlinear measurement function and/or non-Gaussian system disturbance, which complicates the localization of network nodes. In this presentation, a novel particle-assisted stochastic search (PASS) algorithm is proposed to find out the optimal node locations based on its non-convex objective function.

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1 Views

Cooperative localization plays a key role in locationaware service of wireless networks. However, the statistical-based estimator of network localization, e.g., the maximum likelihood estimator or the maximum a posterior estimator, is commonly non-convex due to nonlinear measurement function and/or non-Gaussian system disturbance, which complicates the localization of network nodes. In this presentation, a novel particle-assisted stochastic search (PASS) algorithm is proposed to find out the optimal node locations based on its non-convex objective function.

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7 Views

In this paper, the detection of shock wave that generated by supersonic bullet is considered. We present a new framework based on wavelet multi-scale products method. We analyze the method under the standard likelihood ratio test. It is found that the multi-scale product method is made in an assumption that is extremely restricted, just hold for a special noise condition. Based on the analysis, a general condition is considered for the detection. An optimal detector under the standard likelihood ratio test is proposed.

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4 Views

In this manuscript the application of a factor graph approach to the filtering problem for a mixed linear/nonlinear state-space model is investigated. In particular, after developing a factor graph for the considered model, a novel approximate recursive technique for solving such a problem is derived applying the sum-product algorithm and a specific scheduling procedure for message passing to this graph.

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58 Views

The problem of estimating the frequencies of sinusoidal components from a finite number of noisy discrete-time measurements has attracted a great deal of attention and still is an active research area to date, because of its wide applications in science and engineering. In this presentation, simple and accurate solutions for sinusoidal frequency estimation of 1D and 2D signals in the presence of additive white Gaussian noise are presented.

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644 Views

Finding the position of a target based on measurements from an array of spatially separated sensors has been an important problem in radar, sonar, global positioning system, mobile communications, multimedia and wireless sensor networks. Time-of-arrival (TOA), time-difference-of-arrival (TDOA), received signal strength (RSS) and direction-of-arrival (DOA) of the emitted signal are commonly used measurements for source localization. Basically, TOAs, TDOAs and RSSs provide the distance information between the source and sensors while DOAs are the source bearings relative to the receivers.

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1055 Views

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