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A sensor array processing approach to object region detection

IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING 2001年第11期48卷 1319-1325页

作者： Lei, TH Udupa, JK Univ Penn Dept Radiol Med Image Proc Grp Philadelphia PA 19104 USA

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.

关键词： eigenstructure method image segmentation sensor array processing

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Landmine detection and localization using chemical sensor array processing

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IEEE TRANSACTIONS ON SIGNAL processing 2000年第5期48卷 1295-1305页

作者： Jeremic, A Nehorai, A Univ Illinois Dept Elect Engn & Comp Sci Chicago IL 60607 USA

We develop methods for automatic detection and localization of landmines using chemical sensor arrays and statistical signal processing techniques. The transport of explosive vapors emanating from buried landmines is modeled as a diffusion process in a two-layered system consisting of ground and air. Measurement and statistical models are then obtained from 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. To determine the unknown location of a landmine, we derive a maximum likelihood (ML) estimation algorithm and evaluate its performance by computing the Cramer-Rao bound (CRB). The results are applied to the design of chemical sensor arrays, satisfying criteria specified in terms of detection and estimation performance measures and for optimally selecting the number and positions of sensors and the number of time samples, To illustrate the potential of the proposed techniques in a realistic demining scenario, we derive a moving-sensor algorithm in which the stationary sensor array is replaced by a single moving sensor. Numerical examples are given to demonstrate the applicability of our results.

关键词： landmine detection localization moving sensor sensor array processing

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A STOCHASTIC GEOMETRIC APPROACH TO sensor array processing

A STOCHASTIC GEOMETRIC APPROACH TO SENSOR ARRAY PROCESSING

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IEEE Workshop on Statistical Signal processing (SSP)

作者： Vo, Ba Ngu Vo, Ba Tuong Curtin Univ Dept Elect & Comp Engn Perth WA Australia

ISBN: (纸本)9781479949755

A new unified mathematical framework for sensor array processing is proposed. The proposed framework combines Bayesian estimation with stochastic geometry to accommodate prior information, uncertainty in array parameters, and unknown and stochastically time-varying number of nonstationary sources. A system model for a common signal setting is constructed to demonstrate the proposed framework.

关键词： sensor array processing Bayesian estimation random sets

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Landmine detection and localization using chemical sensor array processing

Landmine detection and localization using chemical sensor ar...

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Detection and Remediation Technologies for Mines and Minelike Targets IV Conference

作者： Jeremic, A Nehorai, A Univ Illinois Chicago IL 60607 USA

ISBN: (纸本)0819431842

We develop methods for the automatic detection and localization of landmines using chemical sensor arrays and statistical signal processing techniques. The transport of explosive vapors emanating from buried landmines is modeled as a diffusion process in a two layered system consisting of ground and air. The measurement and statistical models are derived by exploiting the associated concentration distribution. We derive a generalized likelihood ratio (GLR) detector and evaluate its performance in terms of the probabilities of detection and false alarm. To determine the unknown location of a landmine we derive a maximum likelihood (ML) estimation algorithm and evaluate its performance by computing the Cramer-Rao bound (CRB). The results are applied to the design of chemical sensor arrays, satisfying criteria specified in terms of detection and estimation performance measures, and to optimally select the number and positions of sensors and the number of time samples. To illustrate the potential of the proposed techniques in a realistic demining scenario, we derive a moving sensor algorithm in which the stationary sensor array is replaced by a single moving sensor. Numerical examples are given to demonstrate the applicability of our results.

关键词： landmine detection localization moving sensor sensor array processing

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NONWAVE FIELD processing USING sensor array APPROACH

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SIGNAL processing 1995年第2期44卷 197-210页

作者： GERSHMAN, AB TURCHIN, VI Ruhr-Universität Bochum Lehrstuhl für Signaltheorie D-44780 Germany Institute of Applied Physics of Russian Academy of Science 46 Uljanov Street 603600 Nizhny Novgorod Russian Federation

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 sensor array, 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.

关键词： sensor array processing NONWAVE FIELD CRAMER-RAO BOUNDS CLEAN ALGORITHM DECONVOLUTION

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Projection Versus Prewhitening for EEG Interference Suppression

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IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING 2012年第5期59卷 1329-1338页

作者： Wu, Shun Chi Swindlehurst, A. Lee Wang, Po T. Nenadic, Zoran Univ Calif Irvine Dept Elect Engn & Comp Sci Irvine CA 92697 USA Univ Calif Irvine Dept Biomed Engn Irvine CA 92697 USA

Suppression of strong, spatially correlated background interference is a challenge associated with electroencephalography (EEG) source localization problems. The most common way of dealing with such interference is through the use of a prewhitening transformation based on an estimate of the covariance of the interference plus noise. This approach is based on strong assumptions regarding temporal stationarity of the data, which do not commonly hold in EEG applications. In addition, prewhitening cannot typically be implemented directly due to ill conditioning of the covariance matrix, and ad hoc regularization is often necessary. Using both simulation examples and experiments involving real EEG data with auditory evoked responses, we demonstrate that a straightforward interference projection method is significantly more robust than prewhitening for EEG source localization.

关键词： Electroencephalography (EEG) interference suppression magnetoencephalography (MEG) sensor array processing source localization

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Adaptive filtering algorithm for direction-of-arrival (DOA) estimation with small snapshots

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DIGITAL SIGNAL processing 2019年 94卷 84-95页

作者： Liu, Beiyi Gui, Guan Matsushita, Shin-ya Xu, Li Akita Prefectural Univ Dept Intelligent Mechatron Akita 0150055 Japan Nanjing Univ Posts & Telecommun Coll Telecommun & Informat Engn Nanjing 210003 Jiangsu Peoples R China

The direction-of-arrival (DOA) estimation problem with a few noisy snapshots can be formulated as a problem of finding a joint sparse representation of multiple measurement vectors (MMV), and some algorithms based on compressive sensing (CS), such as the joint l(0) approximation DOA (JLZA-DOA) and Multiple Snapshot Matching Pursuit Direction of Arrival (MSMPDOA) algorithms, have recently been proposed for solving this problem. Compared with the conventional DOA methods, the CS-based methods can achieve super-resolution by using only small number of snapshots, without the necessity of an accurate initialization, with small sensitivity to the correlation of the source signals. However, these CS-based algorithms usually do not work well in low signal-noise ratio (SNR) environment. In addition, the increased number of sensors in massive multiple-input-multiple-output (MIMO) systems lead to a huge matrix, and the matrix inversion operation in each iteration of the CS-based algorithm results in a relatively high computational cost. The purpose of this paper is to propose a novel adaptive filtering algorithm, i.e., the l(2,0)-least mean square (l(2.0)-LMS) algorithm, which can be viewed as a generalization of the l(0)-LMS algorithm for single measurement vector (SMV) problem. Our proposed algorithm incorporates a mixed norm (l(2)(,0)-norm) to treat the joint sparsity and inherits the robustness against noise and the low complexity of the l(0)-LMS algorithm, and can thus work well for massive MIMO systems. Numerical experiments demonstrate that the proposed algorithm can achieve much better estimation performance with a lower computational cost than the existing ones. (C) 2019 Elsevier Inc. All rights reserved.

关键词： Direction-of-arrival estimation sensor array processing Sparse signal representation Adaptive filter Mixed-norm Massive MIMO system

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Performance analysis of joint DOA/TOA estimator

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SIGNAL processing 2004年第8期84卷 1359-1365页

作者： Mrabti, F Elhajjami, M Zouak, M Univ Sidi Mohammed Ben Abdellah Fac Sci & Tech Lab Signaux Syst & Composants UFR Fes 30000 VN Morocco Fac Sci Fes Dhar El Mehraz UFR Automat & Anal Syst Fes 30000 Atlas Morocco

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 sensor array 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.

关键词： sensor array processing parameter estimate mobile communication scattered sources

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Self-Calibration of Acoustic Scalar and Vector sensor arrays

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IEEE TRANSACTIONS ON SIGNAL processing 2023年 71卷 61-75页

作者： Ramamohan, Krishnaprasad Nambur Chepuri, Sundeep Prabhakar Comesana, Daniel Fernandez Leus, Geert Microflown NL-6824 BV Arnhem Netherlands Indian Inst Sci Dept Elect Commun Engn Bengaluru 560012 Karnataka India Delft Univ Technol Fac EEMCS NL-2628 CD Delft Netherlands

In this work, we consider the self-calibration problem of joint calibration and direction-of-arrival (DOA) estimation using acoustic sensor arrays. Unlike many previous iterative approaches, we propose solvers that can be readily used for both linear and non-linear arrays for jointly estimating the sensor gain, phase errors, and the source DOAs. We derive these algorithms for both the conventional element-space and covariance data models. We focus on sparse and regular arrays formed using scalar sensors as well as vector sensors. The developed algorithms are obtained by transforming the underlying non-linear calibration model into a linear model, and subsequently by using convex relaxation techniques to estimate the unknown parameters. We also derive identifiability conditions for the existence of a unique solution to the self-calibration problem. To demonstrate the effectiveness of the developed techniques, numerical experiments, and comparisons to the state-of-the-art methods are provided. Finally, the results from an experiment that was performed in an anechoic chamber using an acoustic vector sensor array are presented to demonstrate the usefulness of the proposed self-calibration techniques.

关键词： Acoustics Measurement uncertainty Acoustic vector sensors direction-of-arrival estimation self-calibration sensor array processing

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Estimation of nominal direction of arrival and angular spread using an array of sensors

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SIGNAL processing 1996年第1-2期50卷 57-69页

作者： Trump, T Ottersten, B ROYAL INST TECHNOL S-10044 STOCKHOLMSWEDEN

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.

关键词： sensor array processing parameter estimation mobile communications

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