It is well known that the conventional eigenvalue-based minimum description length (MDL) approach for source number estimation suffers from high computational load and performs optimally only in the presence of spatia...
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It is well known that the conventional eigenvalue-based minimum description length (MDL) approach for source number estimation suffers from high computational load and performs optimally only in the presence of spatially and temporally white noise. To improve the robustness of the MDL methodology, we propose to utilize the minimum mean square error (MMSE) of the multistage Wiener filter to calculate the required description length for encoding the observed data, instead of relying on the eigenvalues of the data covariance matrix. As there is no need to calculate the covariance matrix and its eigenvalue decomposition, our derived MMSE-based MDL (mMDL) method is also more computationally efficient than the traditional counterparts. Numerical examples are included to demonstrate the robustness of the mMDL detector in nonuniform noise.
In civilian communication systems, the signature sequence of the desired signal in training phase is known to the receiver. In this letter, using the mutual information, we bridge the probability density function and ...
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In civilian communication systems, the signature sequence of the desired signal in training phase is known to the receiver. In this letter, using the mutual information, we bridge the probability density function and minimum mean-square error (MMSE) between the observed data and training sequence of the desired signal, and then employ the MMSE to construct a minimum description length (MDL) criterion for accurate source enumeration. Numerical results demonstrate that the proposed method is superior to existing MDL methods in terms of detection performance particularly for small number of snapshots and/or source angular separation.
In this paper, using the mutual information, we bridge the probability density function and the minimum mean-square error (MMSE) between the observed data and the desired signal, and then employ the MMSE to construct ...
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ISBN:
(纸本)9781849190107
In this paper, using the mutual information, we bridge the probability density function and the minimum mean-square error (MMSE) between the observed data and the desired signal, and then employ the MMSE to construct an MMSE-based MDL criterion for accurate source enumeration. The presented numerical results demonstrate that the proposed method is superior to the existing MDL methods in detection performance.
This correspondence investigates the direction-of-arrival (DOA) estimation of multiple narrowband sources in the presence of nonuniform white noise with an arbitrary diagonal covariance matrix. While both the determin...
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This correspondence investigates the direction-of-arrival (DOA) estimation of multiple narrowband sources in the presence of nonuniform white noise with an arbitrary diagonal covariance matrix. While both the deterministic and stochastic Cramer-Rao bound (CRB) and the deterministic maximum-likelihood (ML) DOA estimator under this model have been derived by Pesavento and Gershman, the stochastic ML DOA estimator under the same setting is still not available in the literature. In this correspondence, a new stochastic ML DOA estimator is derived. Its implementation is based on an iterative procedure which concentrates the log-likelihood function with respect to the signal and noise nuisance parameters in a stepwise fashion. A modified inverse iteration algorithm is also presented for the estimation of the noise parameters. Simulation results have shown that the proposed algorithm is able to provide significant performance improvement over the conventional uniform ML estimator in nonuniform noise environments and require only a few iterations to converge to the nonuniform stochastic CRB.
We present forward modeling solutions in the form of array response kernels for electroencephalography (EEG) and magnetoencephalography (MEG), assuming that a multilayer ellipsoidal geometry approximates the anatomy o...
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We present forward modeling solutions in the form of array response kernels for electroencephalography (EEG) and magnetoencephalography (MEG), assuming that a multilayer ellipsoidal geometry approximates the anatomy of the head and a dipole current models the source. The use of an ellipsoidal geometry is useful in cases for which incorporating the anisotropy of the head is important but a better model cannot be defined. The structure of our forward solutions facilitates the analysis of the inverse problem by factoring the lead field into a product of the current dipole source and a kernel containing the information corresponding to the head geometry and location of the source and sensors. This factorization allows the inverse problem to be approached as an explicit function of just the location parameters, which reduces the complexity of the estimation solution search. Our forward solutions have the potential of facilitating the solution of the inverse problem, as they provide algebraic representations suitable for numerical implementation. The applicability of our models is illustrated with numerical examples on real EEG/MEG data of N20 responses. Our results show that the residual data after modeling the N20 response using a dipole for the source and an ellipsoidal geometry for the head is in average lower than the residual remaining when a spherical geometry is used for the same estimated dipole.
We present the asymptotic performance analysis of the interpolated root-MUSIC and manifold separation (MS) techniques for direction-of-arrival (DOA) estimation in arbitrary non-uniform sensorarrays. Our analysis take...
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ISBN:
(纸本)9781424422401
We present the asymptotic performance analysis of the interpolated root-MUSIC and manifold separation (MS) techniques for direction-of-arrival (DOA) estimation in arbitrary non-uniform sensorarrays. Our analysis takes into account both the effects of a limited number of snapshots and manifold approximation errors.
We present the asymptotic performance analysis of the interpolated root-MUSIC and manifold separation (MS) techniques for direction-of-arrival (DOA) estimation in arbitrary non-uniform sensorarrays. Our analysis take...
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We present the asymptotic performance analysis of the interpolated root-MUSIC and manifold separation (MS) techniques for direction-of-arrival (DOA) estimation in arbitrary non-uniform sensorarrays. Our analysis takes into account both the effects of a limited number of snapshots and manifold approximation errors.
Early detection and estimation of the spread of a biochemical contaminant are major issues in many applications, such as homeland security and pollution monitoring. We present an integrated approach combining the meas...
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Early detection and estimation of the spread of a biochemical contaminant are major issues in many applications, such as homeland security and pollution monitoring. We present an integrated approach combining the measurements given by an array of biochemical sensors with a physical model of the dispersion and statistical analysis to solve these problems and provide system performance measures. We approximate the dispersion model of a contaminant in a realistic environment through numerical simulations of reflected stochastic diffusions describing the microscopic transport phenomena due to wind and chemical diffusion and use the Feynmann-Kac formula. We consider arbitrary complex geometries and account for wind turbulence. Numerical examples are presented for two real-world scenarios: an urban area and an indoor ventilation duct. Localizing the dispersive sources is useful for decontamination purposes and estimation of the cloud evolution. To solve the associated inverse problem, we propose a Bayesian framework based on a random field that is particularly powerful for localizing multiple sources with small amounts of measurements.
We develop a sequential detector for the release of a biochemical substance, with potential applications in environmental security. The proposed detector provides online detection of the appearance of a biochemical so...
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We develop a sequential detector for the release of a biochemical substance, with potential applications in environmental security. The proposed detector provides online detection of the appearance of a biochemical source in realistic complex environments. To obtain optimal performance, we use an integrated approach combining statistical analysis of measurements given by an array of biochemical sensors with a physical model of the dispersion, amenable to arbitrary geometries and wind turbulence. We first focus on formulating a detector that is applicable in the presence of unknown source parameters (e.g., release time, intensity, and location). We then derive a bound on the expected delay before a false detection in order to select the threshold of the test. For a fixed false-alarm rate, we obtain the detection probability of a release as a function of its location and initial concentration. Numerical examples illustrate the applicability of our methods to real-world scenarios of an urban area and indoor ventilation duct.
In this paper, a concept of contrast of eigenvalues (COE) is introduced to describe the diversity of the eigenvalues of signal covariance matrix. Based on the COE, a novel algorithm is proposed to estimate the central...
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In this paper, a concept of contrast of eigenvalues (COE) is introduced to describe the diversity of the eigenvalues of signal covariance matrix. Based on the COE, a novel algorithm is proposed to estimate the central direction of arrival (DOA) and the angular spread of an incoherently distributed (ID) source. Using the proposed method, it is shown that the parameters of not only the narrowly but also the widely spread sources may be accurately obtained with low computational cost. The numerical examples are given to examine the performance of the COE-based estimator under various environments and show that it is indeed effective for both narrowly and widely spread ID sources. (c) 2006 Elsevier B.V. All rights reserved.
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