The Experts below are selected from a list of 4152 Experts worldwide ranked by ideXlab platform
S. Attallah - One of the best experts on this subject based on the ideXlab platform.
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Adaptive Noise Subspace Estimation Algorithm Suitable for VLSI Implementation
IEEE Signal Processing Letters, 2009Co-Authors: Yang Lu, S. AttallahAbstract:The aim of this letter is twofold: 1) to stabilize a fast adaptive Noise Subspace algorithm, which was proposed earlier in the literature under the name of FRANS algorithm and 2) to present a low computational cost version that is free from any square root and division operations, which is very suitable for VLSI implementation. A stability analysis and simulation results are also given in order to assess the performance of this algorithm.
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Speeding up Noise Subspace Estimation Algorithms using an Optimal Diagonal Matrix Step-Size Strategy for MC-CDMA Application
VTC Spring 2008 - IEEE Vehicular Technology Conference, 2008Co-Authors: Lu Yang, S. AttallahAbstract:In this paper, we propose a new optimal diagonal-matrix step-size strategy for some Noise Subspace estimation algorithms. The proposed step-sizes control the decoupled Subspace vectors individually as compared to conventional methods where all the Subspace vectors are multiplied by the same step-size value. Simulation results show that this optimal diagonal- matrix step-size strategy outperforms the original algorithms as it offers faster convergence rate, smaller steady state error and similar orthogonality error simultaneously. Finally, the algorithms with the proposed step-size strategy are used for blind channel estimation in MC-CDMA system.
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SiPS - Stable Noise Subspace Estimation Algorithm Suitable for VLSI Implementation
2007 IEEE Workshop on Signal Processing Systems, 2007Co-Authors: Lu Yang, S. Attallah, George MathewAbstract:The aim of this paper is twofold: 1) to stabilize a fast adaptive Noise Subspace algorithm, which was proposed earlier in the literature under the name of FRANS algorithm; and 2) to present a low computational cost version that is free from any square root and division operations, which is very suitable for VLSI implementation. A theoretical mean-square analysis and simulation results are also given in order to assess the performance of this algorithm.
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Stable Noise Subspace Estimation Algorithm Suitable for VLSI Implementation
2007 IEEE Workshop on Signal Processing Systems, 2007Co-Authors: Lu Yang, S. Attallah, George MathewAbstract:The aim of this paper is twofold: 1) to stabilize a fast adaptive Noise Subspace algorithm, which was proposed earlier in the literature under the name of FRANS algorithm; and 2) to present a low computational cost version that is free from any square root and division operations, which is very suitable for VLSI implementation. A theoretical mean-square analysis and simulation results are also given in order to assess the performance of this algorithm.
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Variable Step-size Based Adaptive Noise Subspace Estimation for Blind Channel Estimation
2006 Asia-Pacific Conference on Communications, 2006Co-Authors: Lu Yang, S. Attallah, George MathewAbstract:The recently proposed HFRANS is a computationally efficient and numerically stable adaptive algorithm for Noise Subspace estimation. To improve the convergence and steady-state performances of HFRANS, this paper proposes a variable step-size strategy, while maintaining the overall computational complexity at O(NP). The proposed strategy is obtained by cleverly combining a gradient step-size adaptation approach with the optimum step-size, which are developed in this paper. Simulation results are provided to illustrate the performance advantages of the proposed strategy. We also study the application of HFRANS with variable step-size to blind channel estimation in MC-CDMA system
Lu Yang - One of the best experts on this subject based on the ideXlab platform.
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Speeding up Noise Subspace Estimation Algorithms using an Optimal Diagonal Matrix Step-Size Strategy for MC-CDMA Application
VTC Spring 2008 - IEEE Vehicular Technology Conference, 2008Co-Authors: Lu Yang, S. AttallahAbstract:In this paper, we propose a new optimal diagonal-matrix step-size strategy for some Noise Subspace estimation algorithms. The proposed step-sizes control the decoupled Subspace vectors individually as compared to conventional methods where all the Subspace vectors are multiplied by the same step-size value. Simulation results show that this optimal diagonal- matrix step-size strategy outperforms the original algorithms as it offers faster convergence rate, smaller steady state error and similar orthogonality error simultaneously. Finally, the algorithms with the proposed step-size strategy are used for blind channel estimation in MC-CDMA system.
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VTC Spring - Speeding up Noise Subspace Estimation Algorithms using an Optimal Diagonal Matrix Step-Size Strategy for MC-CDMA Application
VTC Spring 2008 - IEEE Vehicular Technology Conference, 2008Co-Authors: Lu Yang, S. AttallahAbstract:In this paper, we propose a new optimal diagonal-matrix step-size strategy for some Noise Subspace estimation algorithms. The proposed step-sizes control the decoupled Subspace vectors individually as compared to conventional methods where all the Subspace vectors are multiplied by the same step-size value. Simulation results show that this optimal diagonal- matrix step-size strategy outperforms the original algorithms as it offers faster convergence rate, smaller steady state error and similar orthogonality error simultaneously. Finally, the algorithms with the proposed step-size strategy are used for blind channel estimation in MC-CDMA system.
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SiPS - Stable Noise Subspace Estimation Algorithm Suitable for VLSI Implementation
2007 IEEE Workshop on Signal Processing Systems, 2007Co-Authors: Lu Yang, S. Attallah, George MathewAbstract:The aim of this paper is twofold: 1) to stabilize a fast adaptive Noise Subspace algorithm, which was proposed earlier in the literature under the name of FRANS algorithm; and 2) to present a low computational cost version that is free from any square root and division operations, which is very suitable for VLSI implementation. A theoretical mean-square analysis and simulation results are also given in order to assess the performance of this algorithm.
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Stable Noise Subspace Estimation Algorithm Suitable for VLSI Implementation
2007 IEEE Workshop on Signal Processing Systems, 2007Co-Authors: Lu Yang, S. Attallah, George MathewAbstract:The aim of this paper is twofold: 1) to stabilize a fast adaptive Noise Subspace algorithm, which was proposed earlier in the literature under the name of FRANS algorithm; and 2) to present a low computational cost version that is free from any square root and division operations, which is very suitable for VLSI implementation. A theoretical mean-square analysis and simulation results are also given in order to assess the performance of this algorithm.
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Variable Step-size Based Adaptive Noise Subspace Estimation for Blind Channel Estimation
2006 Asia-Pacific Conference on Communications, 2006Co-Authors: Lu Yang, S. Attallah, George MathewAbstract:The recently proposed HFRANS is a computationally efficient and numerically stable adaptive algorithm for Noise Subspace estimation. To improve the convergence and steady-state performances of HFRANS, this paper proposes a variable step-size strategy, while maintaining the overall computational complexity at O(NP). The proposed strategy is obtained by cleverly combining a gradient step-size adaptation approach with the optimum step-size, which are developed in this paper. Simulation results are provided to illustrate the performance advantages of the proposed strategy. We also study the application of HFRANS with variable step-size to blind channel estimation in MC-CDMA system
George Mathew - One of the best experts on this subject based on the ideXlab platform.
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SiPS - Stable Noise Subspace Estimation Algorithm Suitable for VLSI Implementation
2007 IEEE Workshop on Signal Processing Systems, 2007Co-Authors: Lu Yang, S. Attallah, George MathewAbstract:The aim of this paper is twofold: 1) to stabilize a fast adaptive Noise Subspace algorithm, which was proposed earlier in the literature under the name of FRANS algorithm; and 2) to present a low computational cost version that is free from any square root and division operations, which is very suitable for VLSI implementation. A theoretical mean-square analysis and simulation results are also given in order to assess the performance of this algorithm.
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Stable Noise Subspace Estimation Algorithm Suitable for VLSI Implementation
2007 IEEE Workshop on Signal Processing Systems, 2007Co-Authors: Lu Yang, S. Attallah, George MathewAbstract:The aim of this paper is twofold: 1) to stabilize a fast adaptive Noise Subspace algorithm, which was proposed earlier in the literature under the name of FRANS algorithm; and 2) to present a low computational cost version that is free from any square root and division operations, which is very suitable for VLSI implementation. A theoretical mean-square analysis and simulation results are also given in order to assess the performance of this algorithm.
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Variable Step-size Based Adaptive Noise Subspace Estimation for Blind Channel Estimation
2006 Asia-Pacific Conference on Communications, 2006Co-Authors: Lu Yang, S. Attallah, George MathewAbstract:The recently proposed HFRANS is a computationally efficient and numerically stable adaptive algorithm for Noise Subspace estimation. To improve the convergence and steady-state performances of HFRANS, this paper proposes a variable step-size strategy, while maintaining the overall computational complexity at O(NP). The proposed strategy is obtained by cleverly combining a gradient step-size adaptation approach with the optimum step-size, which are developed in this paper. Simulation results are provided to illustrate the performance advantages of the proposed strategy. We also study the application of HFRANS with variable step-size to blind channel estimation in MC-CDMA system
Jin Ho Choi - One of the best experts on this subject based on the ideXlab platform.
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underdetermined high resolution doa estimation a 2 rho th order source signal Noise Subspace constrained optimization
IEEE Transactions on Signal Processing, 2015Co-Authors: Jin Ho ChoiAbstract:For estimating the direction of arrival (DOA)s of non-stationary source signals such as speech and audio, a constrained optimization problem (COP) that exploits the spatial diversity provided by an array of sensors is formulated in terms of a Noise-eliminated local $2\rho$ th-order cumulant matrix. The COP solution provides a weight vector to the look direction such that it is constrained to the $2\rho$ th-order source-signal Subspace when the look direction is in alignment with the true DOA; otherwise, it is constrained to the $2\rho$ th-order Noise Subspace. This weight vector is incorporated into the spatial spectrum to determine the degree of orthogonality between itself and either the $2\rho$ th-order source-signal Subspace when the number of sources is unknown, or the $2\rho$ th-order Noise Subspace when the number of sources is known. For a uniform linear array (ULA) of $M$ sensors, the spatial spectrum for known number of sources can theoretically be shown to identify up to $2\rho(M-1)$ sources. Realizing the difficulty in identifying stationarity in the received sensor signals, the estimate of the Noise-eliminated local $2\rho$ th-order cumulant matrix is marginalized over various possible stationary segmentations, for a more robust DOA estimation. In this paper, we focus on the use of local second and fourth order cumulants ( $\rho=1$ , 2), and the proposed algorithms when $\rho=1$ outperformed the KR Subspace-based algorithms and also the 4-MUSIC for globally non-stationary, non-Gaussian synthetic data and also for speech/audio in various adverse environments. We verified that the identifiability for $\rho=2$ is improved by two-folds compared to that for $\rho=1$ with an ULA.
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Underdetermined High-Resolution DOA Estimation: A $2\rho$ th-Order Source-Signal/Noise Subspace Constrained Optimization
IEEE Transactions on Signal Processing, 2015Co-Authors: Jin Ho ChoiAbstract:For estimating the direction of arrival (DOA)s of non-stationary source signals such as speech and audio, a constrained optimization problem (COP) that exploits the spatial diversity provided by an array of sensors is formulated in terms of a Noise-eliminated local 2ρth-order cumulant matrix. The COP solution provides a weight vector to the look direction such that it is constrained to the 2ρth-order source-signal Subspace when the look direction is in alignment with the true DOA; otherwise, it is constrained to the 2ρth-order Noise Subspace. This weight vector is incorporated into the spatial spectrum to determine the degree of orthogonality between itself and either the 2ρth-order source-signal Subspace when the number of sources is unknown, or the 2ρth-order Noise Subspace when the number of sources is known. For a uniform linear array (ULA) of M sensors, the spatial spectrum for known number of sources can theoretically be shown to identify up to 2ρ(M-1) sources. Realizing the difficulty in identifying stationarity in the received sensor signals, the estimate of the Noise-eliminated local 2ρth-order cumulant matrix is marginalized over various possible stationary segmentations, for a more robust DOA estimation. In this paper, we focus on the use of local second and fourth order cumulants ( ρ = 1, 2), and the proposed algorithms when ρ = 1 outperformed the KR Subspace-based algorithms and also the 4-MUSIC for globally non-stationary, non-Gaussian synthetic data and also for speech/audio in various adverse environments. We verified that the identifiability for ρ = 2 is improved by two-folds compared to that for ρ = 1 with an ULA.
Zhu Cheng - One of the best experts on this subject based on the ideXlab platform.
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fast communication interference cancellation in gps receiver using Noise Subspace tracking algorithm
Signal Processing, 2011Co-Authors: Rong Wang, Zhu ChengAbstract:In this paper, we address the problem of interference cancellation in global positioning system (GPS) receiver using a two-step approach: Subspace projection technique and maximum signal-to-Noise ratio (MSNR) beamforming. The interference signals can be effectively suppressed by projecting the received signal on the Noise Subspace. Here Noise Subspace tracking algorithm is employed to estimate the Noise Subspace directly. We then apply a beamformer to maximize the signal-to-Noise ratio of the interference-free signal. Simulation results show that our approach can effectively eliminate the strong interference and enhance the performance of the GPS receiver.