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Design of an Adaptive Wideband Beamforming Algorithm for Conformal arrays

IEEE COMMUNICATIONS LETTERS 2014年第11期18卷 1955-1958页

作者： Rasekh, Meysam Seydnejad, Saeid R. Shahid Bahonar Univ Kerman Dept Elect Engn Kerman *** Iran

Wideband conformal arrays are increasingly used in modern communication systems due to their flexibility in attaching to arbitrary surfaces. However, designing such arrays has been limited to pattern synthesis techniques because of the complexity of their geometrical analysis and the different contribution of each sensor in the array response. These techniques are basically non-adaptive data independent methods and are not suitable for dynamic environments. In this letter, the conventional Frost space-time wideband beamforming method and its associated Linearly Constrained Minimum Variance (LCMV) algorithm are employed to develop an adaptive wideband beamforming method for an arbitrarily shaped conformal array. The constraint matrix used in the LCMV algorithm is modified to include the pattern of each individual sensor, thereby incorporating the element responses into the weight adjustment algorithm. A set of angle-frequency constraints are used in the proposed LCMV formulation to achieve the desired frequency-invariant beampattern. It is shown by a comprehensive example of a typical conical array that the desired beampattern can be obtained in an adaptive manner for a time-varying target.

关键词： Conformal array wideband beamforming space-time processing sensor array processing acoustic communication

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Estimating evoked dipole responses in unknown spatially correlated noise with EEG/MEG arrays

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IEEE TRANSACTIONS ON SIGNAL processing 2000年第1期48卷 13-25页

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

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.

关键词： Cramer-Rao bound dipole source EEG evoked responses maximum likelihood parameter estimation MEG sensor array processing unknown noise covariance

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Tracking of multiple moving sources using recursive EM algorithm

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EURASIP JOURNAL ON APPLIED SIGNAL processing 2005年第1期2005卷 50-60页

作者： Chung, PJ Böhme, JF Hero, AO Univ Michigan Dept Elect Engn & Comp Sci Ann Arbor MI 48109 USA Ruhr Univ Bochum Dept Elect Engn & Informat Sci Signal Theory Grp D-44780 Bochum Germany

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.

关键词： sensor array processing recursive estimation moving sources tracking

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Maximum-likelihood direction-of-arrival estimation in the presence of unknown nonuniform noise

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IEEE TRANSACTIONS ON SIGNAL processing 2001年第7期49卷 1310-1324页

作者： Pesavento, M Gershman, AB McMaster Univ Dept Elect & Comp Engn Hamilton ON L8S 4K1 Canada

We consider the problem of estimating directions of arrival (DOAs) of multiple sources observed on the background of nonuniform white noise with an arbitrary diagonal covariance matrix. A new deterministic maximum likelihood (ML) DOA estimator is derived. Its implementation is based on an iterative procedure which includes a stepwise concentration of the log-likelihood (LL) function with respect to the signal and noise nuisance parameters and requires only a few iterations to converge. New closed-form expressions for the deterministic and stochastic direction estimation Cramer-Rao bounds (CRBs) are derived for the considered nonuniform model. Our expressions can be viewed as an extension of the well-known results by Stoica and Nehorai and Weiss and Friedlander to a more general noise model than the commonly used uniform one. In addition, these expressions extend the recent results by Matveyev et al. to the multiple source case. Comparisons with the above-mentioned earlier results help to discover several interesting properties of DOA estimation in the nonuniform noise case, To compare the estimation performance of the proposed ML, technique with the results of our CRB analysis and with the performance of conventional "uniform" ME, simulation results are presented. Additionally, we test our technique using experimental seismic array data. Our simulations and experimental results both validate essential performance improvements achieved by means of the approach proposed.

关键词： direction-of-arrival estimation maximum likelihood algorithm nonuniform noise sensor array processing

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Detection-estimation in sensor arrays without eigendecompositions

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IEICE TRANSACTIONS ON COMMUNICATIONS 1996年第8期E79B卷 1147-1155页

作者： KloucheDjedid, A Miura, R ATR OPT & RADIO COMMUN RES LABS KYOTO 61902 JAPAN

High resolution algorithms in sensor arrays 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.

关键词： antennas and propagation sensor array processing

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Polarimetric adaptive detection in non-Gaussian noise

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SIGNAL processing 2003年第2期83卷 297-306页

作者： De Maio, A Alfano, G Univ Naples Federico II Dipartimento Ingn Elett & Telecommun I-80125 Naples Italy

in this paper we address polarimetric adaptive detection of targets embedded in compound-Gaussian clutter with unknown covariance matrix. To this end we assume that a set of secondary data, free of signal components and with the same covariance structure of the cell under test, is available. We resort to a design procedure based upon the generalized likelihood ratio test (GLRT): first we derive the GLRT assuming that the textures are known, then we plug into the derived test suitable estimates of these parameters. Remarkably, the newly proposed detector has the constant false alarm rate (CFAR) property with respect to the texture statistical characterization. Moreover, even though it does not ensure the CFAR property with respect to the clutter covariance matrix, a sensitivity analysis shows that the probability of false alarm is only slightly affected by variations in the clutter correlation properties. Finally, the performance assessment, conducted via Monte Carlo simulations, confirms the capability of the receiver to operate in real radar scenarios. (C) 2002 Elsevier Science B.V. All rights reserved.

关键词： sensor array processing polarimetric radar adaptive detection

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Direction-of-Arrival Estimation Using a Mixed l_2,0 Norm Approximation

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IEEE TRANSACTIONS ON SIGNAL processing 2010年第9期58卷 4646-4655页

作者： Hyder, Md Mashud Mahata, Kaushik Univ Newcastle Dept Elect Engn Callaghan NSW 2308 Australia

A set of vectors is called jointly sparse when its elements share a common sparsity pattern. We demonstrate how the direction-of-arrival (DOA) estimation problem can be cast as the problem of recovering a joint-sparse representation. We consider both narrowband and broadband scenarios. We propose to minimize a mixed l(2,0) norm approximation to deal with the joint-sparse recovery problem. Our algorithm can resolve closely spaced and highly correlated sources using a small number of noisy snapshots. Furthermore, the number of sources need not be known a priori. In addition, our algorithm can handle more sources than other state-of-the-art algorithms. For the broadband DOA estimation problem, our algorithm allows relaxing the half-wavelength spacing restriction, which leads to a significant improvement in the resolution limit.

关键词： Compressive sampling direction-of-arrival (DOA) joint-sparse multiple measurement vectors sensor array processing sparse representation

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A polarimetric adaptive matched filter

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SIGNAL processing 2001年第12期81卷 2583-2589页

作者： De Maio, A Ricci, G Univ Naples Federico II Dipartimento Ingn Elett & Telecomunicaz I-80125 Naples Italy

In this paper, we address polarimetric adaptive detection of targets embedded in Gaussian noise with unknown covariance matrix. To this end we assume that a set of secondary data, free of signal components and with the same covariance matrix of the cell under test, is available. We resort to a two-step design procedure based upon the generalized likelihood ratio test (GLRT). It is shown that the newly proposed detector has the constant false alarm rate property with respect to the covariance matrix of the noise. More remarkably, it has the same performance, but a lower complexity, than the corresponding plain GLRT. (C) 2001 Elsevier Science B.V. All rights reserved.

关键词： sensor array processing polarimetric radar adaptive detection

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Estimating brain conductivities and dipole source signals with EEG arrays

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IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING 2004年第12期51卷 2113-2122页

作者： Gutiérrez, D Nehorai, A Muravchik, CH Univ Illinois Dept Elect & Comp Engn Chicago IL 60607 USA Univ Illinois Dept Bioengn Chicago IL 60607 USA Univ Nacl La Plata Fac Ingn Dept Elect Lab Elect Ind Control & Instrument La Plata Argentina

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.

关键词： brain conductivities Cramer-Rao bound electroencephalography maximum-likelihood estimation parameter estimation sensor array processing

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ESTIMATION OF SKEW ANGLE IN TEXT-IMAGE ANALYSIS BY SLIDE - SUBSPACE-BASED LINE DETECTION

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MACHINE VISION AND APPLICATIONS 1994年第4期7卷 267-276页

作者： AGHAJAN, HK KHALAJ, BH KAILATH, T 1. Information Systems Laboratory Department of Electrical Engineering Stanford University 94305 Stanford CA USA

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.

关键词： TEXT ANALYSIS SKEW DETECTION OCR LINE FITTING sensor array processing

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