Statistical methods for detecting and estimating biochemical dispersion by a moving source using model-based integrated sensor array processing are developed. Two possible cases are considered: a homogeneous semi-infi...
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Statistical methods for detecting and estimating biochemical dispersion by a moving source using model-based integrated sensor array processing are developed. Two possible cases are considered: a homogeneous semi-infinite medium (corresponding to the environment such as air above the ground for an airborne source) or a two-layer semi-infinite medium (e.g., shallow water). The proposed methods can be extended to more complex scenarios. The goals are to detect and localize the biochemical source, determine the space-time concentration distribution of the dispersion, and predict its cloud evolution. Potential applications include security, environmental monitoring, pollution control, simulating hazardous accidents, and explosives detection. Diffusion models of the biochemical substance concentration distribution are derived under various boundary and environmental conditions. A maximum-likelihood algorithm is used to estimate the biochemical concentration distribution in space and time, and the Cramer-Rao bound is computed to analyze its performance. Two detectors (generalized-likelihood ratio test (GLRT) and a mean-difference detector) are derived and then their performances are determined in terms of the probabilities of detection and false alarm. The results can be used to design the sensorarray for optimal performance. Numerical examples illustrate the results of the concentration distribution and the performances of the proposed methods.
We study the performance of various beamformers for estimating a current dipole source at a known location using electroencephalography (EEG) and magnetoencephalography (MEG). We present our beamformers in the form of...
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We study the performance of various beamformers for estimating a current dipole source at a known location using electroencephalography (EEG) and magnetoencephalography (MEG). We present our beamformers in the form of the generalized sidelobe canceler (GSC). Under this structure, the beamformer can be solved by finding a filter that achieves the minimum mean-squared error (MMSE) between the mainbearn response and filtered observed signal. We express the MMSE as a function of the filter's rank and use it as a criterion to evaluate the performance of the beamformers. We do not make any assumptions on the rank of the interference-plus-noise covariance matrix. Instead, we treat it as low-rank and derive a general expression for the MMSE. We present numerical examples to compare the MSE performance of beamformers commonly studied in the literature: principal components (PCs), cross-spectral metrics (CSMs), and eigencanceler (EIG) beamformers. Our results show that good estimates of the dipole source signals can be achieved using reduced-rank beamformers even for low signal-to-noise ratio (SNR) values.
Identification of finite-impulse-response (FIR) and multiple-input multiple-output (MIMO) channels driven by unknown uncorrelated colored sources is a challenging problem. In this paper, a group decorrelation enhanced...
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Identification of finite-impulse-response (FIR) and multiple-input multiple-output (MIMO) channels driven by unknown uncorrelated colored sources is a challenging problem. In this paper, a group decorrelation enhanced subspace (GDES) method is presented. The GDES method uses the idea of subspace decomposition and signal decorrelation more effectively than the joint diagonalization enhanced subspace (JDES) method previously reported in the literature. The GDES method has a much better performance than the JDES method. The correctness of the GDES method is proved assuming that 1) the channel matrix is irreducible and column reduced and 2) the source spectral matrix has distinct diagonal functions. However, the GDES method has an inherent ability to trade off between the required condition on the channel matrix and that on the source spectral matrix. Simulations show that the GDES method yields good results even when the channel matrix is not irreducible, which is not possible at all for the JDES method.
In this paper, the problem of blind spatial signature estimation using the parallel factor (PARAFAC) analysis model is addressed in application to wireless. communications. A time-varying user power loading in the upl...
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In this paper, the problem of blind spatial signature estimation using the parallel factor (PARAFAC) analysis model is addressed in application to wireless. communications. A time-varying user power loading in the uplink mode is proposed to make the model identifiable and to enable application of PARAFAC analysis. Then, identifiability issues are studied in detail and closed-form expressions for the corresponding modified Cramer-Rao bound (CRB) are obtained. Furthermore, two blind spatial signature estimation algorithms are developed. The first technique is based on the PARAFAC fitting trilinear alternating least squares (TALS) regression procedure, whereas the second one makes use of the joint approximate diagonalization algorithm. These techniques do not require any knowledge of the propagation channel and/or sensorarray manifold and are applicable to a more general class of scenarios than earlier approaches to blind spatial signature estimation.
We present a source localization method based on a sparse representation of sensor measurements with an overcomplete basis composed of samples from the array manifold. We enforce sparsity by imposing penalties based o...
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We present a source localization method based on a sparse representation of sensor measurements with an overcomplete basis composed of samples from the array manifold. We enforce sparsity by imposing penalties based on the l(1)-norm. A number of recent theoretical results on sparsifying properties of l(1) penalties justify this choice. Explicitly enforcing the sparsity of the representation is motivated by a desire to obtain a sharp estimate of the spatial spectrum that exhibits super-resolution. We propose to use the singular value decomposition (SVD) of the data matrix to summarize multiple time or frequency samples. Our formulation leads to an optimization problem, which we solve efficiently in a second-order cone (SOC) programming framework by an interior point implementation. We propose a grid refinement method to mitigate the effects of limiting estimates to a grid of spatial locations and introduce an automatic selection criterion for the regularization parameter involved in our approach. We demonstrate the effectiveness of the method on simulated data by plots of spatial spectra and by comparing the estimator variance to the Cramar-Rao bound (CRB). We observe that our approach has a number of advantages over other source localization techniques, including increased resolution, improved robustness to noise, limitations in data quantity, and correlation of the sources, as well as not requiring an accurate initialization.
We deal with recursive direction-of-arrival (DOA) estimation of multiple moving sources. Based on the recursive EM algorithm, we develop two recursive procedures to estimate the time-varying DOA parameter for narrowba...
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We deal with recursive direction-of-arrival (DOA) estimation of multiple moving sources. Based on the recursive EM algorithm, we develop two recursive procedures to estimate the time-varying DOA parameter for narrowband signals. The first procedure requires no prior knowledge about the source movement. The second procedure assumes that the motion of moving sources is described by a linear polynomial model. The proposed recursion updates the polynomial coefficients when a new data arrives. The suggested approaches have two major advantages: simple implementation and easy extension to wideband signals. Numerical experiments show that both procedures provide excellent results in a slowly changing environment. When the DOA parameter changes fast or two source directions cross with each other, the procedure designed for a linear polynomial model has a better performance than the general procedure. Compared to the beamforming technique based on the same parameterization, our approach is computationally favorable and has a wider range of applications.
In this paper, we demonstrate that the performance of a direction of arrival (DOA) estimator is fundamentally limited by the size of the region over which we measure a wavefield. That is, even assuming continuous fiel...
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ISBN:
(纸本)0780390075
In this paper, we demonstrate that the performance of a direction of arrival (DOA) estimator is fundamentally limited by the size of the region over which we measure a wavefield. That is, even assuming continuous field measurements across the region, we still cannot achieve perfect performance. We use an approach based on modal decomposition of a spatially truncated field, and completely independent of sensor geometry, to derive the Cramer-Rao Bound (CRB) for spatially-limited DOA estimators. The model is validated by comparison with results from a uniform circular array (UCA) as the number of sensors goes to infinity. Simulations of the spatial CRB show how DOA performance improves as the measurement region expands. Simulations of the bound also indicate that P sources can only be effectively resolved once a certain threshold region size is reached.
This paper presents a new approach for medical image analysis. It translates the object region-detection problem into a sensor array processing framework and detects the number of object regions based on the signal ei...
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This paper presents a new approach for medical image analysis. It translates the object region-detection problem into a sensor array processing framework and detects the number of object regions based on the signal eigenstructure of the converted array system. The theoretical and experimental results, obtained by using this approach on various medical images were in good agreement.
In many applications such as radar and mobile communication, the multipath propagation effects are described as a sum of contributions of a large number of wavefronts that arrives at the sensorarray in clusters of ra...
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In many applications such as radar and mobile communication, the multipath propagation effects are described as a sum of contributions of a large number of wavefronts that arrives at the sensorarray in clusters of rays, distributed around a nominal direction of the signal sources. Based on this observation and on the work of Bengtsson and Ottersten (Proceeding of Norsig-98, IEEE Nordic Signal processing Symposium, April 1998), this paper jointly estimate the directions of arrival and the times of arrival of scattered sources. A theoretical performance analysis is given in terms of asymptotic error variance and illustrated by a simulation study. (C) 2004 Elsevier B.V. All rights reserved.
Techniques based on electroencephalography (EEG) measure the electric potentials on the scalp and process them to infer the location, distribution, and intensity of underlying neural activity. Accuracy in estimating t...
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Techniques based on electroencephalography (EEG) measure the electric potentials on the scalp and process them to infer the location, distribution, and intensity of underlying neural activity. Accuracy in estimating these parameters is highly sensitive to uncertainty in the conductivities of the head tissues. Furthermore, dissimilarities among individuals are ignored when standarized values are used. In this paper, we apply the maximum-likelihood and maximum a posteriori (MAP) techniques to simultaneously estimate the layer conductivity ratios and source signal using EEG data. We use the classical 4-sphere model to approximate the head geometry, and assume a known dipole source position. The accuracy of our estimates is evaluated by comparing their standard deviations with the Cramer-Rao bound (CRB). The applicability of these techniques is illustrated with numerical examples on simulated EEG data. Our results show that the estimates have low bias and attain the CRB for sufficiently large number of experiments. We also present numerical examples evaluating the sensitivity to imprecise assumptions on the source position and skull thickness. Finally, we propose extensions to the case of unknown source position and present examples for real data.
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