The Experts below are selected from a list of 1998 Experts worldwide ranked by ideXlab platform
W.h. Ku - One of the best experts on this subject based on the ideXlab platform.
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Behavior of the partial correlation coefficients of a least squares lattice filter in the presence of a Nonstationary chirp Input
IEEE Transactions on Signal Processing, 1995Co-Authors: T. Soni, J.r. Zeidler, W.h. KuAbstract:This paper studies the performance of the a posteriori recursive least squares lattice filter in the presence of a Nonstationary chirp signal. The forward and backward partial correlation (PARCOR) coefficients for a Wiener-Hopf optimal filter are shown to be complex conjugates for the general case of a Nonstationary Input with constant power. Such an optimal filter is compared to a minimum mean square error based least squares lattice adaptive filter. Expressions are found for the behavior of the first stage of the adaptive filter based on the least squares algorithm. For the general nth stage, the PARCOR coefficients of the previous stages are assumed to have attained Wiener-Hopf optimal steady state. The PARCOR coefficients of such a least squares adaptive filter are compared with the optimal coefficients for such a Nonstationary Input. The optimal lattice fitter is seen to track a chirp Input without any error, and the tracking lag in such an adaptive filter is due to the least squares update procedure. The expression for the least squares based PARCOR coefficients are found to contain two terms: a decaying convergence term due to the weighted estimation procedure and a tracking component that asymptotically approaches the optimal coefficient value. The rate of convergence is seen to depend inversely on the forgetting factor. The tracking lag of the filter is derived as a function of the rate of nonstationarity and the forgetting factor. It is shown that for a given chirp rate there is a threshold adaptation constant below which the total tracking error is negligible. For forgetting factors above this threshold, the error increases nonlinearly. Further, this threshold forgetting factor decreases with increasing chirp rate. Simulations are presented to validate the analysis.
J M Cioffi - One of the best experts on this subject based on the ideXlab platform.
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block transmission over dispersive channels transmit filter optimization and realization and mmse dfe receiver performance
IEEE Transactions on Information Theory, 1996Co-Authors: Naofal Aldhahir, J M CioffiAbstract:Optimal transmit filters for packet-based data transmission on dispersive Gaussian-noise linear time-invariant channels are derived by maximizing the mutual information, subject to a fixed Input power budget. A quasi-stationary approximation to the optimal Nonstationary Input covariance process is derived and shown to exhibit negligible mutual information loss from the optimal case, for situations of most practical interest. Moreover, this quasi-stationary approximation results in efficiently computed lattice or pole-zero implementations of the transmit filter. By considering the popular finite-impulse-response minimum-mean-square-error decision-feedback equalizer (FIR MMSE-DFE) as a receiver structure, we show that transmitter optimization results in an appreciable improvement in the decision-point signal-to-noise ratio. Finally, we show that, as the output blocklength becomes infinite, the optimum finite-dimensional Nonstationary Input covariance process converges to a stationary process whose power spectrum obeys the well-known water-pouring distribution.
Lorenzo Galleani - One of the best experts on this subject based on the ideXlab platform.
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Approximating the time-frequency output of a dynamical system for an arbitrary Nonstationary Input
2013 IEEE International Conference on Acoustics Speech and Signal Processing, 2013Co-Authors: Lorenzo GalleaniAbstract:We obtain the approximate analytic time-frequency spectrum of the output of a dynamical system when the Input is an arbitrary finite-energy Nonstationary signal. Our method is based on three steps. First, we transform the dynamical system to the time-frequency domain. Second, we approximate the time-frequency spectrum of the Input as a sum of short duration sinusoids through a Fourier series expansion. Finally, we combine the time-frequency outputs corresponding to each individual short duration sinusoid, which are known in exact analytic form. An example shows that the proposed method requires a few terms only to obtain an approximate time-frequency output which is indistinguishable from the exact one. Furthermore, our method can clarify how dynamical systems process Nonstationary signals. This processing mechanism is of fundamental interest since dynamical systems are a common model for real-world signals.
T. Soni - One of the best experts on this subject based on the ideXlab platform.
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Behavior of the partial correlation coefficients of a least squares lattice filter in the presence of a Nonstationary chirp Input
IEEE Transactions on Signal Processing, 1995Co-Authors: T. Soni, J.r. Zeidler, W.h. KuAbstract:This paper studies the performance of the a posteriori recursive least squares lattice filter in the presence of a Nonstationary chirp signal. The forward and backward partial correlation (PARCOR) coefficients for a Wiener-Hopf optimal filter are shown to be complex conjugates for the general case of a Nonstationary Input with constant power. Such an optimal filter is compared to a minimum mean square error based least squares lattice adaptive filter. Expressions are found for the behavior of the first stage of the adaptive filter based on the least squares algorithm. For the general nth stage, the PARCOR coefficients of the previous stages are assumed to have attained Wiener-Hopf optimal steady state. The PARCOR coefficients of such a least squares adaptive filter are compared with the optimal coefficients for such a Nonstationary Input. The optimal lattice fitter is seen to track a chirp Input without any error, and the tracking lag in such an adaptive filter is due to the least squares update procedure. The expression for the least squares based PARCOR coefficients are found to contain two terms: a decaying convergence term due to the weighted estimation procedure and a tracking component that asymptotically approaches the optimal coefficient value. The rate of convergence is seen to depend inversely on the forgetting factor. The tracking lag of the filter is derived as a function of the rate of nonstationarity and the forgetting factor. It is shown that for a given chirp rate there is a threshold adaptation constant below which the total tracking error is negligible. For forgetting factors above this threshold, the error increases nonlinearly. Further, this threshold forgetting factor decreases with increasing chirp rate. Simulations are presented to validate the analysis.
Naofal Aldhahir - One of the best experts on this subject based on the ideXlab platform.
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block transmission over dispersive channels transmit filter optimization and realization and mmse dfe receiver performance
IEEE Transactions on Information Theory, 1996Co-Authors: Naofal Aldhahir, J M CioffiAbstract:Optimal transmit filters for packet-based data transmission on dispersive Gaussian-noise linear time-invariant channels are derived by maximizing the mutual information, subject to a fixed Input power budget. A quasi-stationary approximation to the optimal Nonstationary Input covariance process is derived and shown to exhibit negligible mutual information loss from the optimal case, for situations of most practical interest. Moreover, this quasi-stationary approximation results in efficiently computed lattice or pole-zero implementations of the transmit filter. By considering the popular finite-impulse-response minimum-mean-square-error decision-feedback equalizer (FIR MMSE-DFE) as a receiver structure, we show that transmitter optimization results in an appreciable improvement in the decision-point signal-to-noise ratio. Finally, we show that, as the output blocklength becomes infinite, the optimum finite-dimensional Nonstationary Input covariance process converges to a stationary process whose power spectrum obeys the well-known water-pouring distribution.
