In this paper, we propose a new algorithm for computing a singular value decomposition of a matrix product. We show that our algorithm is numerically desirable in that all relevant residual elements will be numericall...
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In this paper, we propose a new algorithm for computing a singular value decomposition of a matrix product. We show that our algorithm is numerically desirable in that all relevant residual elements will be numerically small. Our algorithm can be extended to a product of a larger number of upper triangular matrices.
Beam-based adaptive processing is an economical way to achieve good interference rejection performance from an adaptive receiving array, at much less computational cost than full element-based methods. However, to exp...
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Beam-based adaptive processing is an economical way to achieve good interference rejection performance from an adaptive receiving array, at much less computational cost than full element-based methods. However, to exploit this potential for planar arrays it is necessary to identify, in real time, which beams must be retained for adaptive cancellation. This paper analyzes the beam-selection problem and presents a computationally efficient algorithm that performs real-time beam selection.
An iterative solution is given for solving deblurring problems having nonnegativity constraints through the use of methods motivated by tomographic imaging. After briefly reviewing three versions of tomographic-imagin...
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An iterative solution is given for solving deblurring problems having nonnegativity constraints through the use of methods motivated by tomographic imaging. After briefly reviewing three versions of tomographic-imaging problems, the paper indicates how methods that have proven to be powerful for the third version, weighted-integral tomography, can be applied to the more general deblurring problem when nonnegativity constraints are present.
The recently introduced Phase Gradient Autofocus (PGA) algorithm is a non-parametric autofocus technique which has been shown to be quite effective for phase correction of Synthetic Aperture Radar (SAR) imagery. This ...
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This paper describes two techniques for automatic recognition of surface targets from an airborne platform using an imaging laser radar sensor. The first technique rotates a three-dimensional model of the target in re...
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This paper describes two techniques for automatic recognition of surface targets from an airborne platform using an imaging laser radar sensor. The first technique rotates a three-dimensional model of the target in real time to enable a generalized Hough transform to match the ladar image to the target's key discriminating features as a basis for target identification. The second technique uses a variation on minimum average correlation energy filters to perform robust target identification. Examples illustrating the application of these algorithms are presented, along with a description of a real-time implementation of the critical parts of the algorithms on a 40,000-processor systolic array based on the Geometric Arithmetic Parallel Processor (GAPPTM) chip.
In this paper, an adaptive algorithm for direction-finding of correlated sources is presented. The algorithm is low in computational complexity and it does not require determination of the effective rank of the array ...
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In this paper, an adaptive algorithm for direction-finding of correlated sources is presented. The algorithm is low in computational complexity and it does not require determination of the effective rank of the array correlation matrix. The algorithm employs a gradient technique to determine the minimum eigenvector of the correlation matrix and an orthogonalization technique to determine the second minimum eigenvector. The two noise eigenvectors are then used to compute the spatial spectra. The angle of arrivals can then be found by superimposing the two spectra. To verify further the true arrivals, additional spatial spectral can be computed using a combination of the two noise eigenvectors. Numerical results show that the proposed algorithms are effective in resolving correlated sources.
Cumulants, and their associated Fourier transforms, known as polyspectra, are very useful in situations where one or more of the preceding phenomena - non-Gaussianity, nonminimum phase, colored Gaussian noise, and non...
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Cumulants, and their associated Fourier transforms, known as polyspectra, are very useful in situations where one or more of the preceding phenomena - non-Gaussianity, nonminimum phase, colored Gaussian noise, and nonlinearities - are present. Because second-order-based techniques have not led to very useful results in the face of these phenomena, it is no exaggeration to believe that it should be possible to reexamine every application and/or method that has ever made use of second-order statistics, using higher-order statistics, to see if better results can be obtained. The purpose of this paper is to give a brief introduction to cumulants and polyspectra and to give a brief overview of some of their applications.
We develop an algorithm for adaptively estimating the noise subspace of a data matrix, as is required in signalprocessing applications employing the 'signal subspace' approach. The noise subspace is estimated...
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We develop an algorithm for adaptively estimating the noise subspace of a data matrix, as is required in signalprocessing applications employing the 'signal subspace' approach. The noise subspace is estimated using a rank-revealing QR factorization instead of the more expensive singular value or eigenvalue decompositions. Using incremental condition estimation to monitor the smallest singular values of triangular matrices, we can update the rank-revealing triangular factorization inexpensively when new rows are added and old rows are deleted. Experiments demonstrate that the new approach usually requires O(n2) work to update an n × n matrix, and accurately tracks the noise subspace.
A new parallel Jacobi-like algorithm for computing the eigenvalues of a general complex matrix is presented. The asymptotic convergence rate of this algorithm is provably quadratic and this is also demonstrated in num...
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A new parallel Jacobi-like algorithm for computing the eigenvalues of a general complex matrix is presented. The asymptotic convergence rate of this algorithm is provably quadratic and this is also demonstrated in numerical experiments. The algorithm promises to be suitable for real-time signalprocessing applications. In particular, the algorithm can be implemented using n2/4 processors, taking O (n log2 n) time for random matrices.
Current bilinear time-frequency representations apply a fixed kernel to smooth the Wigner distribution. However, the choice of a fixed kernel limits the class of signals that can be analyzed effectively. This paper pr...
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Current bilinear time-frequency representations apply a fixed kernel to smooth the Wigner distribution. However, the choice of a fixed kernel limits the class of signals that can be analyzed effectively. This paper presents optimality criteria for the design of signal-dependent kernels that suppress cross-components while passing as much auto-component energy as possible, irrespective of the form of the signal. A fast algorithm for the optimal kernel solution makes the procedure competitive computationally with fixed kernel methods. Examples demonstrate the superior performance of the optimal kernel for a frequency modulated signal.
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