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Affine Combination
The Experts below are selected from a list of 525 Experts worldwide ranked by ideXlab platform
Tõnu Trump – 1st expert on this subject based on the ideXlab platform
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An Affine Combination of adaptive filters for channels with different sparsity levels
Telfor Journal, 2020Co-Authors: Maksim Butsenko, Tõnu TrumpAbstract:In this paper we present an Affine Combination strategy for two adaptive filters. One filter is designed to handle sparse impulse responses and the other one performs better if impulse response is dispersive. Filter outputs are combined using an adaptive mixing parameter and the resulting output shows a better performance than each of the combining filters separately. We also demonstrate that Affine Combination results in faster convergence than a convex Combination of two adaptive filters
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An Affine Combination of adaptive filters for sparse impulse response identification
2015 23rd Telecommunications Forum Telfor (TELFOR), 2015Co-Authors: Maksim Butsenko, Tõnu TrumpAbstract:In this paper we present an Affine Combination strategy for two adaptive filters. One filter is designed to handle sparse impulse responses and the other one performs better if impulse response is dispersive. Filter outputs are combined using an adaptive mixing parameter and the resulting output shows better performance than each of the combining filters separately. We also demonstrate that Affine Combination results in faster convergence than convex Combination of two adaptive filters.
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an adaptive sensor array using an Affine Combination of two filters
International Conference on Signal Processing, 2012Co-Authors: Tõnu TrumpAbstract:We study an adaptive sensor array that uses a Combination of two filters as the adaptive scheme to update the beamformer weights. The Generalized Sidelobe Canceller configuration is used for computing the adaptive weights of the beamformer. As the adaptive scheme we use the recently proposed Affine Combination of two adaptive filters. The filters are joined with help of an adaptive parameter. This way the scheme forms a two stage adaptive structure. The Combination of two adaptive filters is a new interesting way of obtaining both fast initial convergence and small steady state error at the same time. We present the results of a steady state analysis of the scheme in a sensor array processing scenario. The theoretical results are verified in our simulation study.
Jean-yves Tourneret – 2nd expert 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: Jose 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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An Affine Combination of two LMS adaptive filters – statistical analysis of an error power ratio scheme
2009 Conference Record of the Forty-Third Asilomar Conference on Signals Systems and Computers, 2009Co-Authors: Neil J. Bershad, Jose C. M. Bermudez, Jean-yves TourneretAbstract:A recent paper studied the statistical behavior of an Affine Combination of two LMS adaptive filters that simultaneously adapt on the same inputs. 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 combining parameter ¿(n). A scheme based on the ratio of error powers of the two filters was proposed in. Monte Carlo simulations demonstrated nearly optimum performance for this scheme. The purpose of this paper is to analyze the statistical behavior of such error power scheme. Expressions are derived for the mean behavior of ¿(n) and for the weight mean-square deviation. Monte Carlo simulations show 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, Jose 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).
Vitor H Nascimento – 3rd expert on this subject based on the ideXlab platform
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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, Vitor 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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Knowledge-aided STAP algorithm using Affine Combination of inverse covariance matrices for heterogenous clutter
Sensor Signal Processing for Defence (SSPD 2010), 2010Co-Authors: Rui Fa, Rodrigo C. De Lamare, Vitor H NascimentoAbstract:By incorporating a priori knowledge into radar signal processing architectures, knowledge-aided space-time adaptive processing (KA-STAP) algorithms can offer the potential to substantially enhance detection performance and to combat heterogeneous clutter effects. In this paper, we develop a KA-STAP algorithm to estimate directly the interference covariance matrix inverse rather than the covariance matrix itself, by using a linear Combination of inverse covariance matrices (LCICM), which leads to an equivalent expression of the Combination of two filters. The computational load is greatly reduced due to the avoidance of the matrix inversion operation. The performance of the LCICM scheme can be further improved by applying a modification. Moreover, adaptive algorithms for the mixing parameters are developed using Affine Combinations (AC). Numerical examples show the potential of our proposed algorithms for substantial performance improvement.
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On Combinations of CMA equalizers
2009 IEEE International Conference on Acoustics Speech and Signal Processing, 2009Co-Authors: Renato Candido, Magno T M Silva, Vitor H NascimentoAbstract:We extend the Affine Combination of one fast and one slow least mean-square (LMS) filter to blind equalization, considering the Combination of two constant modulus algorithms (CMA). We analyze the proposed Combination in stationary and nonstationary environments verifying that there are situations where the absence of the restriction on the mixing parameter can be advantageous for the Combination. Furthermore, we propose a Combination of two CMAs with different initializations. Preliminary simulations show that this scheme can avoid local minima and eventually can present a faster convergence rate than that of its components.