The Experts below are selected from a list of 5715 Experts worldwide ranked by ideXlab platform
M. Barton - One of the best experts on this subject based on the ideXlab platform.
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Performance of SVD-based fractionally spaced equalizers in data transmission systems
IEEE Transactions on Signal Processing, 1994Co-Authors: M. BartonAbstract:An algorithm is presented for implementing a complex fractionally spaced equalizer (CFSE) that uses the least-mean-square (LMS) algorithm and singular value decomposition (SVD). SVD is used to reduce the eigenvalue spread of the Autocorrelation Matrix of the CFSE tap inputs. It is shown that SVD accelerates the convergence of the CFSE in proportion to the receiver oversampling factor but maintains the steady-state excess mean square error (MSE) at approximately the same level as that of the CFSE that does not have an SVD-based receiver prefilter. The authors choice of a prefilter from the Autocorrelation Matrix of the received signal will facilitate easier and faster tracking of the principal components in an adaptive environment. >
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ICASSP - An alternative realization of the SVD-based FSE
International Conference on Acoustics Speech and Signal Processing, 1Co-Authors: M. BartonAbstract:An investigation is conduced of the noise threshold performance of the singular-value-decomposition (SVD)-based least-mean-square (LMS) adaptive algorithm when the Autocorrelation Matrix of the input signal is not truly rank deficient and when low-rank approximation techniques are used to prefilter the channel output adaptively. It is shown that SVD improves the input SNR (signal-to-noise ratio) and reduces the eigenvalue spread of the Autocorrelation Matrix, but these improvements fall short of the amount required to significantly improve the convergence characteristics of the LMS fractionally spaced equalizer operating in low SNRs. The results show some improvement in the initial convergence rate of the algorithm, while the excess mean-squared error (MSE) remains essentially unchanged. >
G Vazquezgrau - One of the best experts on this subject based on the ideXlab platform.
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recursive bayes risk parameter estimation from the cyclic Autocorrelation Matrix
International Conference on Acoustics Speech and Signal Processing, 1994Co-Authors: J Ribasagarra, G VazquezgrauAbstract:We present a new method for recursively estimating the frequency and timing parameters of a second order cyclostationary signal with known cyclic Autocorrelation Matrix (CAM). This problem appears in the context of radar as well as asynchronous communications in highly non-stationary environments (e.g. telemetry) due to the Doppler effect. The parameters evolution is modelled by a zero-order random walk. The estimates are obtained from the instantaneous CAM (ICAM) of the signal. While nonlinear Kalman filter theory is a possible approach to this problem, Bayes risk theory is used instead. In this way the Riccati equation and gain matrices become independent of the estimates, thus allowing a look-up table solution. The folded normal density (FND) is assumed as parameters' prior. The prior is recursively updated in mean (estimates) and variance. By comparing the obtained equations with linear Kalman filter equations we show that with few modifications a first-order random walk model can be easily incorporated to cope with highly non-stationary frequency evolution. >
G. Vazquez-grau - One of the best experts on this subject based on the ideXlab platform.
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ICASSP (4) - Recursive Bayes risk parameter estimation from the cyclic Autocorrelation Matrix
Proceedings of ICASSP '94. IEEE International Conference on Acoustics Speech and Signal Processing, 1Co-Authors: J. Riba-sagarra, G. Vazquez-grauAbstract:We present a new method for recursively estimating the frequency and timing parameters of a second order cyclostationary signal with known cyclic Autocorrelation Matrix (CAM). This problem appears in the context of radar as well as asynchronous communications in highly non-stationary environments (e.g. telemetry) due to the Doppler effect. The parameters evolution is modelled by a zero-order random walk. The estimates are obtained from the instantaneous CAM (ICAM) of the signal. While nonlinear Kalman filter theory is a possible approach to this problem, Bayes risk theory is used instead. In this way the Riccati equation and gain matrices become independent of the estimates, thus allowing a look-up table solution. The folded normal density (FND) is assumed as parameters' prior. The prior is recursively updated in mean (estimates) and variance. By comparing the obtained equations with linear Kalman filter equations we show that with few modifications a first-order random walk model can be easily incorporated to cope with highly non-stationary frequency evolution. >
Saleem A. Kassam - One of the best experts on this subject based on the ideXlab platform.
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A unified approach to coherent source decorrelation by Autocorrelation Matrix smoothing
Signal Processing, 1995Co-Authors: Richard J. Kozick, Saleem A. KassamAbstract:Abstract A wide variety of techniques have been developed to deal with coherent signals at a sensor array. Many of these techniques have the structure of a pre-processor followed by a standard adaptive beamforming or angle of arrival estimation algorithm. The pre-processor is designed to reduce the cross-correlations between the arriving signals. A general decorrelation technique of this type called Autocorrelation Matrix smoothing (AMS) is described in this paper. The processing in AMS consists of a two-dimensional linear filtering operation on the correlation Matrix of the array measurements. A number of the previously proposed decorrelation techniques can be interpreted as special cases of AMS, corresponding to different choices for the mask used to filter the correlation Matrix. Although the previous methods were not originally formulated in terms of AMS, the unified interpretation points out relations between the techniques and suggests improved techniques. This paper summarizes the basic properties of AMS, explains the unified interpretation of previous decorrelation methods, and describes some extensions for improved decorrelation.
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coherent source location using Autocorrelation Matrix smoothing with transmit receive arrays
Southeastern Symposium on System Theory, 1994Co-Authors: Richard J. Kozick, Saleem A. KassamAbstract:When arrays of narrowband transmitting and receiving elements are used to locate the directions of remote targets, the reflections from the targets are often spatially coherent. Angle of arrival (AOA) estimation techniques such as MUSIC are not effective in this situation. A spatial smoothing operation can be performed on the sum coarray of the transmit/receive system in order to decorrelate the coherent reflections, and two previously-proposed methods are of this type. In the present paper, a more general decorrelation method called Autocorrelation Matrix smoothing is applied to transmit/receive arrays. >
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Improved coherent source decorrelation by Autocorrelation Matrix smoothing
Proceedings of 27th Asilomar Conference on Signals Systems and Computers, 1Co-Authors: Richard J. Kozick, Saleem A. KassamAbstract:Many adaptive beamforming and angle of arrival estimation algorithms perform poorly when the signals arriving at the array are mutually correlated or coherent. We describe an improved technique for coherent signal decorrelation that is based on the idea of Autocorrelation Matrix smoothing (AMS). We begin with a brief review of the definition and properties of AMS, including a unified interpretation of several previously-proposed decorrelation methods as special cases of AMS. Then we use the AMS approach to formulate an improved decorrelation technique, and conclude with computer simulations that illustrate the performance of the new technique. >
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Coherent source location using Autocorrelation Matrix smoothing with transmit/receive arrays
Proceedings of 26th Southeastern Symposium on System Theory, 1Co-Authors: Richard J. Kozick, Saleem A. KassamAbstract:When arrays of narrowband transmitting and receiving elements are used to locate the directions of remote targets, the reflections from the targets are often spatially coherent. Angle of arrival (AOA) estimation techniques such as MUSIC are not effective in this situation. A spatial smoothing operation can be performed on the sum coarray of the transmit/receive system in order to decorrelate the coherent reflections, and two previously-proposed methods are of this type. In the present paper, a more general decorrelation method called Autocorrelation Matrix smoothing is applied to transmit/receive arrays. >
N Tepedenlengilou - One of the best experts on this subject based on the ideXlab platform.
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a fast technique for finding the stationary Autocorrelation Matrix from a perturbational measurement of the gradient
International Conference on Acoustics Speech and Signal Processing, 1993Co-Authors: R D Roberts, N TepedenlengilouAbstract:It is shown that, when using a synchronously spaced FIR (finite impulse response) data equalizer with complex weights, one can estimate the Autocorrelation Matrix by empirically measuring the gradient of the MSE (mean square error) performance surface using only the real portion of the maximal time-delay weight located at the far end of the FIR filter. Since one only needs to perturb one weight of the FIR filter to find the Autocorrelation Matrix, this represents a minimal computation technique. This technique is of particular usefulness when one does not have access to the tap weight data vector. >
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ICASSP (3) - A fast technique for finding the stationary Autocorrelation Matrix from a perturbational measurement of the gradient
IEEE International Conference on Acoustics Speech and Signal Processing, 1993Co-Authors: R D Roberts, N TepedenlengilouAbstract:It is shown that, when using a synchronously spaced FIR (finite impulse response) data equalizer with complex weights, one can estimate the Autocorrelation Matrix by empirically measuring the gradient of the MSE (mean square error) performance surface using only the real portion of the maximal time-delay weight located at the far end of the FIR filter. Since one only needs to perturb one weight of the FIR filter to find the Autocorrelation Matrix, this represents a minimal computation technique. This technique is of particular usefulness when one does not have access to the tap weight data vector. >
