An interference cancellation technique that combines a signal subspace approach with adaptive adjustment of the weight vector is derived. The weight vector is constrained to lie in a signal subspace computed by eigend...
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A high resolution algorithm is presented for resolving multiple incoherent and coherent plane waves incident on an array of sensors. The incident sources may be a mixture of narrowband and broadband sources, and, the ...
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The Wigner Distribution Function (WDF) is a time-frequency descriptor capable of tracking the time-varying second order statistics in a signal. In this paper, we characterize linear systems in terms of the WDFs of the...
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In adaptive least-squares estimation problems, a desired signal d(n) is estimated using a linear combination of L observation values samples x1(ri), X2,(n),..., L-I(n) and denoted by the vector X(n). The estimate is f...
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In adaptive least-squares estimation problems, a desired signal d(n) is estimated using a linear combination of L observation values samples x1(ri), X2,(n),..., L-I(n) and denoted by the vector X(n). The estimate is formed as the inner product of this vector with a corresponding L dimensional weight vector W. One particular weight vector of interest is Wopt which minimizes the mean-square between d(n) and the estimate. In this context, the term 'mean-square difference' is a quadratic measure such as statistical expectation or time average. The specific value of W which achieves the minimum is given by the product of the inverse data covariance matrix and the crosscorrelation between the data vector and the desired signal. The latter is often referred to as the P-vector. For those cases in which time samples of both the desired and data vector signals are available, a variety of adaptive methods have been proposed which will guarantee that an iterative weight vector Wa (n) converges (in some sense) to the optimal solution. Two which have been extensively studied are the recursive least-squares (RLS) method and the LMS gradient approximation approach. There are several problems of interest in the communication and radar environment in which the optimal leastsquares weight set is of interest and in which time samples of the desired signal are not available. Examples can be found in array processing in which only the direction of arrival of the desired signal is known and in single channel filtering where the spectrum of the desired response is known a priori. One approach to these problems which has been suggested is the P-vector algorithm which is an LMSlike approximate gradient method. Although it is easy to derive the mean and variance of the weights which result with this algorithm, there has never been an identification of the corresponding underlying error surface which the procedure searches. The purpose of this paper is to suggest an alternative approach to pro
This paper presents a brief overview of the speech recognition technology. algorithms such as Isolated Word Recognition, Dynamic Time Warping, Hidden Markov Modeling, Vector Quantization, Connected Word Recognition, a...
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The presentation will consider the following issues with the aim of highlighting possible initial research directions. (i) Starting point: in particular, the question of which formulation of the Kalman filter to emplo...
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The presentation will consider the following issues with the aim of highlighting possible initial research directions. (i) Starting point: in particular, the question of which formulation of the Kalman filter to employ as a basis for a parallel implementation will be addressed. (ii) Concurrency: the issue of how to formulate the Kalman filtering algorithm in a parallel format will be discussed. Reported work on the so-called 'common element' and 'common basis' approaches will be critically compared. (iii) Architecture evaluation: factors for making a performance comparison between different parallel architectures will be discussed.
This paper briefly reviews Honeywell progress and capabilities in the development and production of Data Management Systems, Data Recording, and Automatic Target Cuers. The semiconductor technology being applied by Ho...
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ISBN:
(纸本)0892527293
This paper briefly reviews Honeywell progress and capabilities in the development and production of Data Management Systems, Data Recording, and Automatic Target Cuers. The semiconductor technology being applied by Honeywell to the DMS and autocuer circuitry is also briefly reviewed. The necessary advanced fabrication technology is all available at Honeywell's signalprocessing Technologies Center and includes VLSI and VHSIC Phase ii implementations of dense high speed image processing chips. CMOS, Bipolar Enhanced MOS (BEMOS), Digital Bipolar, and Linear Bipolar designs in both silicon and GaAs are used as appropriate. Progress on the algorithms needed to operate the DMS and autocuer hardware is also noted. Laboratory demonstrations of some hardware and algorithms have been done in 1986. Further development in all areas is underway for 1987 and 1988 demonstrations.
The proceedings contain 33 papers. The topics discussed include: signalprocessing computational needs;the use of pivoting to improve the numerical performance of toeplitz solvers;stability, strong stability, and weak...
The proceedings contain 33 papers. The topics discussed include: signalprocessing computational needs;the use of pivoting to improve the numerical performance of toeplitz solvers;stability, strong stability, and weak stability of algorithms for solving linear equations;a conjugate gradient method for the solution of equality constrained least squares problems;parallel QR decomposition of toeplitz matrices;on the implementation of a fully parallel algorithm for the symmetric eigenvalue problem;systolic array computation of the SVD of complex matrices;highly parallel eigenvector update methods with applications to signalprocessing;a systolic array for linearly constrained least-squares problems;analysis of a recursive least squares signalprocessing algorithm;and a subspace approach to determining sensor gain and phase with applications to array processing.
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