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Darren B. Ward, Australian National University (Australia)
Rodney A. Kennedy, Australian National University (Australia)
Robert C.. Williamson, Australian National University (Australia)
Frequency invariant beamforming is array processing in which the spatial response remains constant (with respect to frequency) within a wide frequency band of interest. In this paper we present a new algorithm for adaptive broadband beamforming which solves a minimum variance beamforming problem, with a structural frequency invariant beampattern constraint. This constraint allows us to reduce the dimension of the adaptation problem. The proposed algorithm is a block adaptive LMS algorithm which uses only a fraction of the parameters of a conventional fully adaptive array. Hence, the computational complexity is reduced and the convergence speed is increased. A simulation example is presented to demonstrate the new algorithm.
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Rodney A. Kennedy, Australian National University (Australia)
Darren B. Ward, Australian National University (Australia)
Thushara Abhayapala, Arthur C. Clarke Centre (Sri Lanka)
We establish the asymptotic equivalence, up to complex conjugation, of two problems: (i) determining the nearfield performance of a farfield beampattern specification, and (ii) determining the equivalent farfield beampattern corresponding to a nearfield beampattern specification. Using this reciprocity relationship we develop a computationally simple procedure to design a beamforming array to achieve a desired nearfield beampattern response. The superiority of this approach to existing methods, both in ease of design implementation and performance obtained, is illustrated by a design example.
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Tharmalingam Ratnarajah, Imperial College, London (U.K.)
Athanassios Manikas, Imperial College, London (U.K.)
The idea of applying H^(infinity) estimation techniques to the ``array uncertainties problem'' is motivated by the fact that H^(infinity) estimation is robust to model uncertainties and lack of statistical information with respect to noise. In this paper, a new state space model for the received signal of a general array of sensors is developed which, in contrast to existing models, is capable of handling the simultaneous presence of different type of uncertainties (e.g., gain, phase, locations, mutual coupling, etc, uncertainties). Based on this state-space model, formulated in an H^(infinity) framework, two new robust array signal processing techniques have been proposed which mitigate the degrading effects of array uncertainties.
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Chong-Meng Samson See, DSO (Singapore)
Boon-Kiat Poh Poh, DSO (Singapore)
Colin F.N. Cowan, Queen's University of Belfast (Northern Ireland)
We present a maximum likelihood approach for calibrating sensor arrays in the presence of mutual coupling, channels gain and phase mismatch and array geometry uncertainties using measured steering vectors of uncertain locations. The estimated perturbation parameters is used to calibrate the array manifold, hence enabling many high resolution array processing algorithms to attain their potential advantages. We present two methods for optimizing the highly nonlinear and multimodal ML cost function. The first method is linearized local gradient search algorithm. The second method is derived from combining the fast local search of gradient methods with the nonlinear global search ability of Genetic algorithm. The resulting hybrid optimizer is both fast and globally converging. Simulation results are presented to illustrate the usefulness of the proposed approach.
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Stuart J. Anderson, DSTO (Australia)
Yuri I. Abramovich, CSSIP, South Australia (Australia)
Giuseppe A. Fabrizio, CSSIP, South Australia (Australia)
This paper considers the use of spatio-temporal adaptive processing (STAP) in OTHR and SAR applications to remove nonstationary multipath interference, known as "hot clutter". Since the spatio-temporal properties of hot clutter cannot be assumed constant over the coherent processing interval, conventional adaptive routines fail to provide effective hot clutter mitigation without degrading sub-clutter visibility for backscattered radar signals. The approach presented incorporates multiple stochastic constraints, previously investigated for spatial-only adaptive processing, to achieve effective elimination of hot clutter with distortionless coherent processing for the backscattered radar signals.
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Yuri I. Abramovich, CSSIP (Australia)
Nicholas K. Spencer, CSSIP (Australia)
Alexei Y. Gorokhov, Télécom Paris (France)
We investigate DOA (direction-of-arrival) estimation for arbitrary linear arrays, where the antenna positions may be non-integer values in half-wavelength units. We introduce an approach based on arbitrary virtual linear arrays to resolve manifold ambiguity and estimate DOA's in the superior case. These virtual arrays adopt the set of covariance lags specified by the original array and so themselves have an incomplete set of covariance lags. A maximum entropy completion algorithm for the partially-specified Hermitian covariance matrix is proposed. This is followed by an algorithm which searches for a fixed number of plane wavefronts ("generalised Pisarenko completion"). The variety of possible virtual array geometries also permits a "randomised" approach, whereby the DOA estimates are determined as the stable point of partial solutions calculated over the set of particular virtual geometries. Numerical simulations demonstrate the high efficiency of manifold ambiguity resolution, and a remarkable proximity to the Cramer-Rao bound for DOA estimation.
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Ruby Tweg, RAFAEL (Israel)
Mati Wax, RAFAEL (Israel)
We present an algorithm for direction-of-arrival tracking that allows operation below the ambiguity threshold of the direction finding system. Using multiple target tracking techniques, the algorithm turns the most likely directions-of-arrival of each measurement into multiple potential tracks and then selects the true track as that with the maximum cumulative likelihood. The improvement offered by the algorithm, namely the extension of the ambiguity-free domain, is demonstrated by simulated experiments.
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Ta-Hsin Li, Texas A&M University (U.S.A.)
Jerry D. Gibson, Texas A&M University (U.S.A.)
A new method of nonstationary signal analysis, called time-correlation analysis (TCA), is applied to a class of nonstationary random signals containing time distortion. The method reveals a relationship between the TCA summary statistics and the distortion and leads to two nonparametric estimators for the distortion function. An example is given that demonstrates the application of the TCA method to motion compensation problems in radar imaging.
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John W. Pierre, University of Wyoming (U.S.A.)
Ernest D. Scott, University of Wyoming (U.S.A.)
Michael P. Hays, University of Wyoming (U.S.A.)
This paper describes the University of Wyoming Source Tracking Array Testbed, UW STAT. This system was specifically developed to perform experimental evaluation of source tracking algorithms which use high resolution direction finding algorithms in conjunction with an updating of the signal or the noise subspace after each snapshot. The array is a six element uniformly spaced linear array. UW STAT is a compact testbed and allows for precision source motion. The motion is controlled by a motor and the angular position is recorded by an encoder, thus allowing for a direct comparison of the tracking algorithm's performance with the true position recorded by the encoder. Initial experimental tracking results presented in the paper demonstrate the tracking performance. UW STAT information and experimental data is available on the World Wide Web at http://wwweng.uwyo.edu/electrical/array.html.
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Edward Baranoski, MIT Lincoln Laboratory (U.S.A.)
James Ward, MIT Lincoln Laboratory (U.S.A.)
Maximum likelihood (ML) parameter estimation for multi-dimensional adaptive problems is addressed. Multiple adaptive outputs are ordinarily combined by utilizing the full dimension data. However, many adaptive problems utilize subspace processing for each adaptive beam which can increase the difficulty of many super-resolution techniques. This paper shows that steering vector structure can be utilized to allow ML techniques for a fixed grid of hypothesis vectors to be computationally feasible for many scenarios.
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Wing Ip Tam, University of Toronto (Canada)
Dimitrios Hatzinakos, University of Toronto (Canada)
In radar tracking target motion is best modeled in Cartesian coordinates. Its position is however measured in polar coordinates (range and azimuth). Tracking in Cartesian coordinates with noisy polar measurements requires either converting the measurements to a Cartesian frame of reference and then applying the linear Kalman filter to the converted measurement or using the extended Kalman filter (EKF) in mixed coordinates. The first approach is accurate only for moderate cross-range errors; the second approach is consistent only for small errors. A new efficient tracking algorithm using the multidimensional Gauss-Hermite quadratures to propagate the mean and the covariance of the conditional probability density function is presented. This method is compared with the EKF and the converted measurement Kalman filter (CMKF) and it is shown to be more accurate.
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Madiha Sabry-Rizk, City University (U.K.)
Walid Zgallai, City University (U.K.)
Paul Hardiman, North Middlesex Hospital (U.K.)
John O'Riordan, North Middlesex Hospital (U.K.)
This paper utilises the distinctive transient pulse feature of the Third-Order Cumulant Diagonal Slice (TOCDS) of ECG signals to detect the number of occurrences of fetal heart beats during each of the maternal cycles. The fetal ECG signal is a comparatively weak signal (less than 20 percent of the mother ECG) and often embedded in noise. The fetal heart rate (FHR) lies in the range from 1.3 Hz to 3.5 Hz and it is possible for the mother and some of the fetal ECG signals to be closely overlapping. The paper also addresses the problem of false alarm situations often arising in diagonal-cumulant-slice recording during labour due to non-Gaussian impulsive or transient noise types and shows some results obtained after Volterra noise whitening and interpolation of the neighbouring fetal TOCDS samples.
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Ali Mohammad Djafari, LSS, CNRS-SUPELEC-UPS (France)
Yassine Khayi, LSS, CNRS-SUPELEC-UPS (France)
We consider the problem of the shape reconstruction of a compact object in X ray tomography when the contour of the object is modeled by a polygon. The problem is then to estimate the vertices of that polygon from a limited number of projections. The main objectives of this paper are: -- to show how this reconstruction problem becomes equivalent to a generic mathematical inversion problem which arises also in linear antenna Array Processing (AP); -- to evaluate the performances of the classical AP techniques to handle with this reconstruction problem, and, -- to propose a new method based on Bayesian estimation approach for the resolution of this inverse problem.
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Pascal Chevalier, TCC/UTTC/TSI (France)
Alain Maurice, TCC/UTTC/TSI (France)
The classical Linearly Constrained Minimum Variance (LCMV) Beamformer corresponds, in the general case, to the Linear, Time Invariant (TI) and Spatio-Temporal (ST) complex filter which output power is minimized under some linear constraints. Optimal for stationary signals, this Beamformer becomes sub-optimal for (quasi)-cyclostationary observations for which the optimal complex filters are (poly)-periodic (PP) and, under some conditions of non circularity, Widely Linear (WL). Using these results and the fact that PP filtering is equivalent to FREquency SHifted (FRESH) filtering, the purpose of this paper is to present a first extension of the classical LCMV Beamformer, taking into account the potential (quasi)-cyclostationarity and non circularity properties of the observations. This new Cyclic LCMV Beamformer is shown to have an equivalent Cyclic Generalized Sidelobe Canceller (GSLC) structure. The performance computation of this new Cyclic Beamformer shows the interest of the latter in cyclostationary contexts and opens a reflexion about the optimal constraint choice.
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