We present maximum likelihood (ML) methods for estimating evoked dipole responses using electroencephalography (EEG) and magnetoencephalography (MEG) arrays, which allow for spatially correlated noise between sensors ...
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We present maximum likelihood (ML) methods for estimating evoked dipole responses using electroencephalography (EEG) and magnetoencephalography (MEG) arrays, which allow for spatially correlated noise between sensors with unknown covariance. The electric source is modeled as a collection of current dipoles at fixed locations and the head as a spherical conductor. We permit the dipoles' moments to vary with time by modeling them as linear combinations of parametric or nonparametric etc. basis functions. We estimate the dipoles' locations and moments and derive the Cramer-Rao bound for the unknown parameters. We also propose an ML-based method for scanning the brain response data, which can be used to initialize the multidimensional search required to obtain the true dipole location estimates. Numerical simulations demonstrate the performance of the proposed methods.
We consider the classical problem of fitting a model composed of multiple superimposed signals to noisy data using the criteria of maximum likelihood (ML) or subspace fitting, jointly termed generalized subspace fitti...
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We consider the classical problem of fitting a model composed of multiple superimposed signals to noisy data using the criteria of maximum likelihood (ML) or subspace fitting, jointly termed generalized subspace fitting (GSF), We analyze a recently proposed: approximate dynamic programming algorithm (ADP), which provides a computationally efficient solution to the associated multidimensional multimodal optimization problem. We quantify the error introduced by the approximations in ADP and deviations from the key local interaction signal model (LISMO) modeling assumption in two ways. First, we upper bound the difference between the exact minimum of the GSF criterion and its value at the ADP estimate and compare the ADP with GSF estimates obtained by exhaustive multidimensional search on a fine,lattice. Second, motivated by the similar accuracy bounds, we use perturbation analysis to derive approximate expressions for the MSE of the ADP estimates. These various results provide, for the first time, an effective tool to predict the performance of the ADP algorithm for various signal models at nonasymptotic conditions of interest in practical applications. In particular, they :demonstrate that for the classical problems of sinusoid retrieval and arrayprocessing, ADP performs comparably to enact (but expensive) maximum likelihood (ML) over a wide range df signal-to-noise ratios (SNR's) and is therefore an attractive algorithm.
The problem of direction of arrival estimation in the presence of colored noise with unknown covariance is considered, The unknown noise covariance is assumed to obey a linear parametric model. Using this model, the m...
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The problem of direction of arrival estimation in the presence of colored noise with unknown covariance is considered, The unknown noise covariance is assumed to obey a linear parametric model. Using this model, the maximum likelihood directions parameter estimate is derived, and a large sample approximation is formed. It is shown that a priori information on the source signal correlation structure is easily incorporated into this approximate ML (AML) estimator. Furthermore, a closed-form expression of the Cramer-Rao bound on the direction parameter is provided. A perturbation analysis with respect to a small error in the assumed noise model is carried out, and an expression of the asymptotic bias due to the model mismatch is given. Computer simulations and an application of the proposed technique to a full-scale passive sonar experiment is provided to illustrate the results.
The University of Wyoming Source Tracking array Testbed (UW STAT) was developed as a platform to experimentally investigate recent advances in sensorarray source tracking algorithms. UW STAT is a six sensorarray wit...
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The University of Wyoming Source Tracking array Testbed (UW STAT) was developed as a platform to experimentally investigate recent advances in sensorarray source tracking algorithms. UW STAT is a six sensorarray with one or more sources which may be moving. This system is unique in its compactness and its emphasis on the source tracking problem with the ability to precisely know the true source location for comparison with array tracking algorithm results. Experimental tracking results are presented to demonstrate the performance of the system. Experimental data, along with information about UW STAT, is available on the World Wide Web (http://***/electrical/***) for other researchers to test their tracking algorithms. (C) 1999 Elsevier Science B.V. All rights reserved.
We develop methods for automatic environmental monitoring of disposal sites on the deep ocean door using chemical sensorarrays and statistical hypothesis testing. Such sites have been proposed to relocate dredge mate...
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We develop methods for automatic environmental monitoring of disposal sites on the deep ocean door using chemical sensorarrays and statistical hypothesis testing. Such sites have been proposed to relocate dredge materials from harbors and shipping channels. The transport of pollutants is modeled as a diffusion process, and the measurement and statistical models are derived by exploiting the spatial and temporal evolution of the associated concentration distribution. We derive two detectors, the generalized likelihood ratio (GLR) test and the mean detector, and determine their performance in terms of the probabilities of false alarm and detection. The results are applied to the design of chemical sensorarrays satisfying criteria specified in terms of these probabilities, and to optimally select a number of sensors and time samples. Numerical examples are used to demonstrate the applicability of our results.
Source-tracking algorithms typically require estimation of an array's unknown correlation matrix. Time-averaging forms the basis of estimating the correlation matrix in most applications. This paper provides a pre...
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ISBN:
(纸本)0780350103
Source-tracking algorithms typically require estimation of an array's unknown correlation matrix. Time-averaging forms the basis of estimating the correlation matrix in most applications. This paper provides a preliminary investigation in the use of B-splines rather than time-averages to estimate the correlation matrix. The smooth, continuous character of splines can accurately match the behaviour of an array's true correlation matrix. Preliminary results indicate tracking estimates based on the spline-fit correlation:matrix have improved smoothness over time-averaging methods and can have comparable or better accuracy. Furthermore, B-splines are computationally efficient and are easily adapted to near real-time processing.
The problem of estimating the nominal direction of arrival and angular spread of a source surrounded by a large number of local scatterers using an array of sensors is addressed. This type of propagation occurs freque...
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The problem of estimating the nominal direction of arrival and angular spread of a source surrounded by a large number of local scatterers using an array of sensors is addressed. This type of propagation occurs frequently in, for example, mobile communications. The maximum likelihood estimator is considered and two computationally less complex estimators are also proposed. They are based on least-squares fits of the sample covariance to the theoretical covariance matrix derived from the measurement model. The performance of the least-squares-based algorithm is analyzed and based on this, an optimally weighted least-squares criterion is proposed. The weighted least-squares algorithm is shown to be asymptotically efficient. Results of numerical experiments are presented to indicate small sample behavior of the estimators. The nominal direction-of-arrival (DOA) estimates are compared with those provided by a standard subspace algorithm. Finally, the methods are applied to experimental array data to determine spread angles for non line of sight scenarios.
High resolution algorithms in sensorarrays lead to accurate results but with expensive eigendecompositions making its use in real-time applications such as mobile communications relatively difficult. In this paper, a...
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High resolution algorithms in sensorarrays lead to accurate results but with expensive eigendecompositions making its use in real-time applications such as mobile communications relatively difficult. In this paper, a trade-off between accuracy and computational load is accomplished through a simplified algorithm which instead of eigendecompositions, uses the robust QR decomposition for which many efficient parallel (systolic, wavefront array) implementations exist. First, a simple detection scheme is presented and, through simulations, is shown to work very well for sufficient SNR, even when signals are coherent. Outputs of the detection process include simultaneously estimates of signals Direction Of Arrivals (DOA's) and a simple beamformer vector resulting in an estimate of the desired signal. Extensive simulations are performed assuming different scenarios of variations in SNR, DOA's leading to discussions on the possibilities and limitations of the proposed solution.
The application of sensor array processing methods for estimation and localization of wavefield sources is well known and has been intensively studied in literature. In this paper we extend sensor array processing app...
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The application of sensor array processing methods for estimation and localization of wavefield sources is well known and has been intensively studied in literature. In this paper we extend sensor array processing approach to estimating the parameters of the fields of nonwave nature (the so-called nonwave fields). Considering the static and the diffusion field as typical examples of nonwave fields, and assuming that measurements are carried out by an antenna array, we derive the Cramer-Rao bounds of source parameter estimation errors. These theoretical results are completed by the experimental results of localization of the diffusion sources in distilled water by chemical sensorarray, showing high performance of sensor array processing approach to the problem considered. A modified version of the well-known CLEAN deconvolution algorithm has been used for experimental data processing.
A new signal processing method is developed for estimating the skew angle in text document images. Detection of the skew angle is an important step in text processing tasks such as optical character recognition (OCR) ...
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A new signal processing method is developed for estimating the skew angle in text document images. Detection of the skew angle is an important step in text processing tasks such as optical character recognition (OCR) and computerized filing. Based on a recently introduced multiline-fitting algorithm, the proposed method reformulates the skew detection problem into a special parameter-estimation framework such that a signal structure similar to the one in the field of sensor array processing is obtained. In this framework, straight lines in an image are modeled as wavefronts of propagating planar waves. Certain measurements are defined in this virtual propagation environment such that the large amount of coherency that exists between the locations of the pixels on parallel lines is exploited to enhance a subspace in the space spanned by the measurements. The well-studied techniques of sensor array processing (e.g., the ESPRIT algorithm) are then exploited to produce a closed form and high-resolution estimate for the skew angle.
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