The Experts below are selected from a list of 55335 Experts worldwide ranked by ideXlab platform
Ali H. Sayed - One of the best experts on this subject based on the ideXlab platform.
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combinations of Adaptive Filters performance and convergence properties
IEEE Signal Processing Magazine, 2016Co-Authors: Jeronimo Arenasgarcia, Magno T M Silva, Vítor H. Nascimento, Luis A Azpicuetaruiz, Ali H. SayedAbstract:Adaptive Filters are at the core of many signal processing applications, ranging from acoustic noise supression to echo cancelation [1], array beamforming [2], channel equalization [3], to more recent sensor network applications in surveillance, target localization, and tracking. A trending approach in this direction is to recur to in-network distributed processing in which individual nodes implement adaptation rules and diffuse their estimation to the network [4], [5].
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Adaptive Filters
2008Co-Authors: Ali H. SayedAbstract:Adaptive Filters Adaptive filtering is a topic of immense practical and theoretical value, having applications in areas ranging from digital and wireless communications to biomedical systems. Now, preserving the style and main features of the earlier award-winning publication, Fundamentals of Adaptive Filtering (2005 Terman Award), the author offers readers and instructors a concentrated, systematic, and up-to-date treatment of the subject in this valuable new book. Adaptive Filters allows readers to gain a gradual and solid introduction to the subject, its applications to a variety of topical problems, existing limitations, and extensions of current theories. The book consists of eleven partseach part containing a series of focused lectures and ending with bibliographic comments, problems, and computer projects with MATLAB solutions available to all readers. Additional features include: Numerous tables, figures, and projects Special focus on geometric constructions, physical intuition, linear-algebraic concepts, and vector notation Background material on random variables, linear algebra, and complex gradients collected in three introductory chapters Complete solutions manual available for instructors MATLAB solutions available for all computer projects Adaptive Filters offers a fresh, focused look at the subject in a manner that will entice students, challenge experts, and appeal to practitioners and instructors.
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Fixed‐point steady‐state analysis of Adaptive Filters
International Journal of Adaptive Control and Signal Processing, 2003Co-Authors: N.r. Yousef, Ali H. SayedAbstract:The steady-state performance of Adaptive Filters can vary significantly when they are implemented in finite precision arithmetic, which makes it vital to analyse their performance in a quantized environment. Such analyses can become difficult for Adaptive algorithms with non-linear update equations. This paper develops a feedback and energy-conservation approach to the steady-state analysis of quantized Adaptive algorithms that bypasses some of the difficulties encountered by traditional approaches. Copyright © 2003 John Wiley & Sons, Ltd.
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Transient analysis of Adaptive Filters with error nonlinearities
IEEE Transactions on Signal Processing, 2003Co-Authors: Tareq Y. Al-naffouri, Ali H. SayedAbstract:The paper develops a unified approach to the transient analysis of Adaptive Filters with error nonlinearities. In addition to deriving earlier results in a unified manner, the approach also leads to new performance results without restricting the regression data to being Gaussian or white. The framework is based on energy-conservation arguments and avoids the need for explicit recursions for the covariance matrix of the weight-error vector.
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transient analysis of data normalized Adaptive Filters
IEEE Transactions on Signal Processing, 2003Co-Authors: Tareq Y Alnaffouri, Ali H. SayedAbstract:This paper develops an approach to the transient analysis of Adaptive Filters with data normalization. Among other results, the derivation characterizes the transient behavior of such Filters in terms of a linear time-invariant state-space model. The stability, of the model then translates into the mean-square stability of the Adaptive Filters. Likewise, the steady-state operation of the model provides information about the mean-square deviation and mean-square error performance of the Filters. In addition to deriving earlier results in a unified manner, the approach leads to stability and performance results without restricting the regression data to being Gaussian or white. The framework is based on energy-conservation arguments and does not require an explicit recursion for the covariance matrix of the weight-error vector.
Vítor H. Nascimento - One of the best experts on this subject based on the ideXlab platform.
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combinations of Adaptive Filters performance and convergence properties
IEEE Signal Processing Magazine, 2016Co-Authors: Jeronimo Arenasgarcia, Magno T M Silva, Vítor H. Nascimento, Luis A Azpicuetaruiz, Ali H. SayedAbstract:Adaptive Filters are at the core of many signal processing applications, ranging from acoustic noise supression to echo cancelation [1], array beamforming [2], channel equalization [3], to more recent sensor network applications in surveillance, target localization, and tracking. A trending approach in this direction is to recur to in-network distributed processing in which individual nodes implement adaptation rules and diffuse their estimation to the network [4], [5].
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Chapter 12 – Adaptive Filters
Academic Press Library in Signal Processing, 2014Co-Authors: Vítor H. Nascimento, Magno T M SilvaAbstract:This chapter provides an introduction to Adaptive signal processing, covering basic principles through the most important recent developments. After a brief example for, we present an overview of how Adaptive Filters work, in which we use only deterministic arguments and concepts from basic linear systems theory, followed by a description of a few common applications of Adaptive Filters. Later, we turn to a more general model for Adaptive Filters based on stochastic processes and optimum estimation. Then three of the most important Adaptive algorithms—LMS, NLMS, and RLS—are derived and analyzed in detail. The chapter closes with a brief description of some important extensions to the basic Adaptive filtering algorithms and some promising current research topics. Since Adaptive filter theory brings together results from several fields, short reviews are provided for most of the necessary material in separate sections, that the reader may consult if needed.
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dcd rls Adaptive Filters with penalties for sparse identification
IEEE Transactions on Signal Processing, 2013Co-Authors: Yuriy Zakharov, Vítor H. NascimentoAbstract:In this paper, we propose a family of low-complexity Adaptive filtering algorithms based on dichotomous coordinate descent (DCD) iterations for identification of sparse systems. The proposed algorithms are appealing for practical designs as they operate at the bit level, resulting in stable hardware implementations. We introduce a general approach for developing Adaptive Filters with different penalties and specify it for exponential and sliding window RLS. We then propose low-complexity DCD-based RLS Adaptive Filters with the lasso, ridge-regression, elastic net, and penalties that attract sparsity. We also propose a simple recursive reweighting of the penalties and incorporate the reweighting into the proposed Adaptive algorithms to further improve the performance. For general regressors, the proposed algorithms have a complexity of operations per sample, where is the filter length. For transversal Adaptive Filters, the algorithms require only operations per sample. A unique feature of the proposed algorithms is that they are well suited for implementation in finite precision, e.g., on FPGAs. We demonstrate by simulation that the proposed algorithms have performance close to the oracle RLS performance.
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transient and steady state analysis of the affine combination of two Adaptive Filters
IEEE Transactions on Signal Processing, 2010Co-Authors: Renato Candido, Magno T M Silva, Vítor H. NascimentoAbstract:In this paper, we propose an approach to the transient and steady-state analysis of the affine combination of one fast and one slow Adaptive Filters. The theoretical models are based on expressions for the excess mean-square error (EMSE) and cross-EMSE of the component Filters, which allows their application to different combinations of algorithms, such as least mean-squares (LMS), normalized LMS (NLMS), and constant modulus algorithm (CMA), considering white or colored inputs and stationary or nonstationary environments. Since the desired universal behavior of the combination depends on the correct estimation of the mixing parameter at every instant, its adaptation is also taken into account in the transient analysis. Furthermore, we propose normalized algorithms for the adaptation of the mixing parameter that exhibit good performance. Good agreement between analysis and simulation results is always observed.
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improving the tracking capability of Adaptive Filters via convex combination
IEEE Transactions on Signal Processing, 2008Co-Authors: Magno T M Silva, Vítor H. NascimentoAbstract:As is well known, Hessian-based Adaptive Filters (such as the recursive-least squares algorithm (RLS) for supervised Adaptive filtering, or the Shalvi-Weinstein algorithm (SWA) for blind equalization) converge much faster than gradient-based algorithms [such as the least-mean-squares algorithm (LMS) or the constant-modulus algorithm (CMA)]. However, when the problem is tracking a time-variant filter, the issue is not so clear-cut: there are environments for which each family presents better performance. Given this, we propose the use of a convex combination of algorithms of different families to obtain an algorithm with superior tracking capability. We show the potential of this combination and provide a unified theoretical model for the steady-state excess mean-square error for convex combinations of gradient- and Hessian-based algorithms, assuming a random-walk model for the parameter variations. The proposed model is valid for algorithms of the same or different families, and for supervised (LMS and RLS) or blind (CMA and SWA) algorithms.
Jean-yves Tourneret - One of the best experts on this subject based on the ideXlab platform.
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stochastic analysis of an error power ratio scheme applied to the affine combination of two lms Adaptive Filters
Signal Processing, 2011Co-Authors: José C. M. Bermudez, Neil J. Bershad, Jean-yves TourneretAbstract:The affine combination of two Adaptive Filters that simultaneously adapt on the same inputs has been actively investigated. In these structures, the filter outputs are linearly combined to yield a performance that is better than that of either filter. Various decision rules can be used to determine the time-varying parameter for combining the filter outputs. A recently proposed scheme based on the ratio of error powers of the two Filters has been shown by simulation to achieve nearly optimum performance. The purpose of this paper is to present a first analysis of the statistical behavior of this error power scheme for white Gaussian inputs. Expressions are derived for the mean behavior of the combination parameter and for the Adaptive weight mean-square deviation. Monte Carlo simulations show good to excellent agreement with the theoretical predictions.
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ICASSP - On performance bounds for an affine combination of two LMS Adaptive Filters
2008 IEEE International Conference on Acoustics Speech and Signal Processing, 2008Co-Authors: Neil J. Bershad, José C. M. Bermudez, Jean-yves TourneretAbstract:This paper studies the statistical behavior of an affine combination of the outputs of two LMS Adaptive Filters that simultaneously adapt using the same white Gaussian input. The purpose of the combination is to obtain an LMS Adaptive filter with fast convergence and small steady-state mean-square error (MSE). The linear combination studied is a generalization of the convex combination, in which the combination factor is restricted to the interval (0,1). The viewpoint is taken that each of the two Filters produces dependent estimates of the unknown channel. Thus, there exists a sequence of optimal affine combining coefficients which minimizes the MSE. The optimal unrealizable affine combiner is studied and provides the best possible performance for this class. Then, a new scheme is proposed for practical applications. It is shown that the practical scheme yields close-to-optimal performance when properly designed (as suggested by the theoretical optimal).
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an affine combination of two lms Adaptive Filters transient mean square analysis
IEEE Transactions on Signal Processing, 2008Co-Authors: Neil J. Bershad, José C. M. Bermudez, Jean-yves TourneretAbstract:This paper studies the statistical behavior of an affine combination of the outputs of two least mean-square (LMS) Adaptive Filters that simultaneously adapt using the same white Gaussian inputs. The purpose of the combination is to obtain an LMS Adaptive filter with fast convergence and small steady-state mean-square deviation (MSD). The linear combination studied is a generalization of the convex combination, in which the combination factor lambda(n) is restricted to the interval (0,1). The viewpoint is taken that each of the two Filters produces dependent estimates of the unknown channel. Thus, there exists a sequence of optimal affine combining coefficients which minimizes the mean-square error (MSE). First, the optimal unrealizable affine combiner is studied and provides the best possible performance for this class. Then two new schemes are proposed for practical applications. The mean-square performances are analyzed and validated by Monte Carlo simulations. With proper design, the two practical schemes yield an overall MSD that is usually less than the MSDs of either filter.
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An affine combination of two LMS Adaptive Filters - Transient mean-square analysis
IEEE Transactions on Signal Processing, 2008Co-Authors: Neil J. Bershad, José C. M. Bermudez, Jean-yves TourneretAbstract:This paper studies the statistical behavior of an affine combination of the outputs of two LMS Adaptive Filters that simultaneously adapt using the same white Gaussian inputs. The purpose of the combination is to obtain an LMS Adaptive filter with fast convergence and small steady-state mean-square deviation (MSD). The linear combination studied is a generalization of the convex combination, in which the combination factor $\lambda(n)$ is restricted to the interval $(0,1)$. The viewpoint is taken that each of the two Filters produces dependent estimates of the unknown channel. Thus, there exists a sequence of optimal affine combining coefficients which minimizes the MSE. First, the optimal unrealizable affine combiner is studied and provides the best possible performance for this class. Then two new schemes are proposed for practical applications. The mean-square performances are analyzed and validated by Monte Carlo simulations. With proper design, the two practical schemes yield an overall MSD that is usually less than the MSD's of either filter.
Magno T M Silva - One of the best experts on this subject based on the ideXlab platform.
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combinations of Adaptive Filters performance and convergence properties
IEEE Signal Processing Magazine, 2016Co-Authors: Jeronimo Arenasgarcia, Magno T M Silva, Vítor H. Nascimento, Luis A Azpicuetaruiz, Ali H. SayedAbstract:Adaptive Filters are at the core of many signal processing applications, ranging from acoustic noise supression to echo cancelation [1], array beamforming [2], channel equalization [3], to more recent sensor network applications in surveillance, target localization, and tracking. A trending approach in this direction is to recur to in-network distributed processing in which individual nodes implement adaptation rules and diffuse their estimation to the network [4], [5].
-
Chapter 12 – Adaptive Filters
Academic Press Library in Signal Processing, 2014Co-Authors: Vítor H. Nascimento, Magno T M SilvaAbstract:This chapter provides an introduction to Adaptive signal processing, covering basic principles through the most important recent developments. After a brief example for, we present an overview of how Adaptive Filters work, in which we use only deterministic arguments and concepts from basic linear systems theory, followed by a description of a few common applications of Adaptive Filters. Later, we turn to a more general model for Adaptive Filters based on stochastic processes and optimum estimation. Then three of the most important Adaptive algorithms—LMS, NLMS, and RLS—are derived and analyzed in detail. The chapter closes with a brief description of some important extensions to the basic Adaptive filtering algorithms and some promising current research topics. Since Adaptive filter theory brings together results from several fields, short reviews are provided for most of the necessary material in separate sections, that the reader may consult if needed.
-
transient and steady state analysis of the affine combination of two Adaptive Filters
IEEE Transactions on Signal Processing, 2010Co-Authors: Renato Candido, Magno T M Silva, Vítor H. NascimentoAbstract:In this paper, we propose an approach to the transient and steady-state analysis of the affine combination of one fast and one slow Adaptive Filters. The theoretical models are based on expressions for the excess mean-square error (EMSE) and cross-EMSE of the component Filters, which allows their application to different combinations of algorithms, such as least mean-squares (LMS), normalized LMS (NLMS), and constant modulus algorithm (CMA), considering white or colored inputs and stationary or nonstationary environments. Since the desired universal behavior of the combination depends on the correct estimation of the mixing parameter at every instant, its adaptation is also taken into account in the transient analysis. Furthermore, we propose normalized algorithms for the adaptation of the mixing parameter that exhibit good performance. Good agreement between analysis and simulation results is always observed.
-
improving the tracking capability of Adaptive Filters via convex combination
IEEE Transactions on Signal Processing, 2008Co-Authors: Magno T M Silva, Vítor H. NascimentoAbstract:As is well known, Hessian-based Adaptive Filters (such as the recursive-least squares algorithm (RLS) for supervised Adaptive filtering, or the Shalvi-Weinstein algorithm (SWA) for blind equalization) converge much faster than gradient-based algorithms [such as the least-mean-squares algorithm (LMS) or the constant-modulus algorithm (CMA)]. However, when the problem is tracking a time-variant filter, the issue is not so clear-cut: there are environments for which each family presents better performance. Given this, we propose the use of a convex combination of algorithms of different families to obtain an algorithm with superior tracking capability. We show the potential of this combination and provide a unified theoretical model for the steady-state excess mean-square error for convex combinations of gradient- and Hessian-based algorithms, assuming a random-walk model for the parameter variations. The proposed model is valid for algorithms of the same or different families, and for supervised (LMS and RLS) or blind (CMA and SWA) algorithms.
Neil J. Bershad - One of the best experts on this subject based on the ideXlab platform.
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stochastic analysis of an error power ratio scheme applied to the affine combination of two lms Adaptive Filters
Signal Processing, 2011Co-Authors: José C. M. Bermudez, Neil J. Bershad, Jean-yves TourneretAbstract:The affine combination of two Adaptive Filters that simultaneously adapt on the same inputs has been actively investigated. In these structures, the filter outputs are linearly combined to yield a performance that is better than that of either filter. Various decision rules can be used to determine the time-varying parameter for combining the filter outputs. A recently proposed scheme based on the ratio of error powers of the two Filters has been shown by simulation to achieve nearly optimum performance. The purpose of this paper is to present a first analysis of the statistical behavior of this error power scheme for white Gaussian inputs. Expressions are derived for the mean behavior of the combination parameter and for the Adaptive weight mean-square deviation. Monte Carlo simulations show good to excellent agreement with the theoretical predictions.
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ICASSP - On performance bounds for an affine combination of two LMS Adaptive Filters
2008 IEEE International Conference on Acoustics Speech and Signal Processing, 2008Co-Authors: Neil J. Bershad, José C. M. Bermudez, Jean-yves TourneretAbstract:This paper studies the statistical behavior of an affine combination of the outputs of two LMS Adaptive Filters that simultaneously adapt using the same white Gaussian input. The purpose of the combination is to obtain an LMS Adaptive filter with fast convergence and small steady-state mean-square error (MSE). The linear combination studied is a generalization of the convex combination, in which the combination factor is restricted to the interval (0,1). The viewpoint is taken that each of the two Filters produces dependent estimates of the unknown channel. Thus, there exists a sequence of optimal affine combining coefficients which minimizes the MSE. The optimal unrealizable affine combiner is studied and provides the best possible performance for this class. Then, a new scheme is proposed for practical applications. It is shown that the practical scheme yields close-to-optimal performance when properly designed (as suggested by the theoretical optimal).
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an affine combination of two lms Adaptive Filters transient mean square analysis
IEEE Transactions on Signal Processing, 2008Co-Authors: Neil J. Bershad, José C. M. Bermudez, Jean-yves TourneretAbstract:This paper studies the statistical behavior of an affine combination of the outputs of two least mean-square (LMS) Adaptive Filters that simultaneously adapt using the same white Gaussian inputs. The purpose of the combination is to obtain an LMS Adaptive filter with fast convergence and small steady-state mean-square deviation (MSD). The linear combination studied is a generalization of the convex combination, in which the combination factor lambda(n) is restricted to the interval (0,1). The viewpoint is taken that each of the two Filters produces dependent estimates of the unknown channel. Thus, there exists a sequence of optimal affine combining coefficients which minimizes the mean-square error (MSE). First, the optimal unrealizable affine combiner is studied and provides the best possible performance for this class. Then two new schemes are proposed for practical applications. The mean-square performances are analyzed and validated by Monte Carlo simulations. With proper design, the two practical schemes yield an overall MSD that is usually less than the MSDs of either filter.
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An affine combination of two LMS Adaptive Filters - Transient mean-square analysis
IEEE Transactions on Signal Processing, 2008Co-Authors: Neil J. Bershad, José C. M. Bermudez, Jean-yves TourneretAbstract:This paper studies the statistical behavior of an affine combination of the outputs of two LMS Adaptive Filters that simultaneously adapt using the same white Gaussian inputs. The purpose of the combination is to obtain an LMS Adaptive filter with fast convergence and small steady-state mean-square deviation (MSD). The linear combination studied is a generalization of the convex combination, in which the combination factor $\lambda(n)$ is restricted to the interval $(0,1)$. The viewpoint is taken that each of the two Filters produces dependent estimates of the unknown channel. Thus, there exists a sequence of optimal affine combining coefficients which minimizes the MSE. First, the optimal unrealizable affine combiner is studied and provides the best possible performance for this class. Then two new schemes are proposed for practical applications. The mean-square performances are analyzed and validated by Monte Carlo simulations. With proper design, the two practical schemes yield an overall MSD that is usually less than the MSD's of either filter.
