The Experts below are selected from a list of 5091 Experts worldwide ranked by ideXlab platform
Thomas Kailath - One of the best experts on this subject based on the ideXlab platform.
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numerically stable fast Transversal Filters for recursive least squares adaptive filtering
IEEE Transactions on Signal Processing, 1991Co-Authors: Dirk Slock, Thomas KailathAbstract:A solution is proposed to the long-standing problem of the numerical instability of fast recursive least squares Transversal filter (FTF) algorithms with exponential weighting, an important class of algorithms for adaptive filtering. A framework for the analysis of the error propagation in FTF algorithms is first developed; within this framework, it is shown that the computationally most efficient 7N form is exponentially unstable. However, by introducing redundancy into this algorithm, feedback of numerical errors becomes possible; a judicious choice of the feedback gains then leads to a numerically stable FTF algorithm with a complexity of 8N multiplications and additions per time recursion. The results are presented for the complex multichannel joint-process filtering problem. >
Helmut Schutze - One of the best experts on this subject based on the ideXlab platform.
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a stabilized fast Transversal Filters algorithm for recursive least squares adaptive filtering
Signal Processing, 1994Co-Authors: Z Ren, Helmut SchutzeAbstract:Abstract In this paper, the numerical stability of the well known gain-normalized fast Transversal Filters (GFTF) algorithm is analytically investigated. Starting from an analysis of the error propagation, suitable round-off errors difference equations (REDE) are derived. The stabilization of the GFTF algorithm can be achieved by stabilizing the REDE. The resulting algorithm is numerically stable, having only 7N +27 multiplications and divisions per recursion (MADPR). The numerical stability of the proposed algorithm is demonstrated by the simulation results.
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easy and effective stabilisation measure for fast recursive least squares algorithms for adaptive Transversal Filters
Electronics Letters, 1991Co-Authors: Helmut Schutze, Z RenAbstract:Fast recursive least squares (FRLS) algorithms are known for their favourable convergence characteristics. They are of special interest for the fast adaptation of Transversal Filters of high order N. Unfortunately FRLS algorithms have a tendency towards numeric instability. A new and effective stabilisation measure is presented for a 0(7N) FRLS algorithm which needs only one additional multiplication per iteration step.
Dirk Slock - One of the best experts on this subject based on the ideXlab platform.
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numerically stable fast Transversal Filters for recursive least squares adaptive filtering
IEEE Transactions on Signal Processing, 1991Co-Authors: Dirk Slock, Thomas KailathAbstract:A solution is proposed to the long-standing problem of the numerical instability of fast recursive least squares Transversal filter (FTF) algorithms with exponential weighting, an important class of algorithms for adaptive filtering. A framework for the analysis of the error propagation in FTF algorithms is first developed; within this framework, it is shown that the computationally most efficient 7N form is exponentially unstable. However, by introducing redundancy into this algorithm, feedback of numerical errors becomes possible; a judicious choice of the feedback gains then leads to a numerically stable FTF algorithm with a complexity of 8N multiplications and additions per time recursion. The results are presented for the complex multichannel joint-process filtering problem. >
Z Ren - One of the best experts on this subject based on the ideXlab platform.
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a stabilized fast Transversal Filters algorithm for recursive least squares adaptive filtering
Signal Processing, 1994Co-Authors: Z Ren, Helmut SchutzeAbstract:Abstract In this paper, the numerical stability of the well known gain-normalized fast Transversal Filters (GFTF) algorithm is analytically investigated. Starting from an analysis of the error propagation, suitable round-off errors difference equations (REDE) are derived. The stabilization of the GFTF algorithm can be achieved by stabilizing the REDE. The resulting algorithm is numerically stable, having only 7N +27 multiplications and divisions per recursion (MADPR). The numerical stability of the proposed algorithm is demonstrated by the simulation results.
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easy and effective stabilisation measure for fast recursive least squares algorithms for adaptive Transversal Filters
Electronics Letters, 1991Co-Authors: Helmut Schutze, Z RenAbstract:Fast recursive least squares (FRLS) algorithms are known for their favourable convergence characteristics. They are of special interest for the fast adaptation of Transversal Filters of high order N. Unfortunately FRLS algorithms have a tendency towards numeric instability. A new and effective stabilisation measure is presented for a 0(7N) FRLS algorithm which needs only one additional multiplication per iteration step.
Bede Liu - One of the best experts on this subject based on the ideXlab platform.
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an analysis of lms adaptive two sided Transversal Filters
International Conference on Acoustics Speech and Signal Processing, 1991Co-Authors: M J Reed, Bede LiuAbstract:Adaptive smoothing Filters have been studied for the removal of narrow-band interference and for use in spectral estimation. The authors study the convergence of two LMS (least mean square) adaptive smoothers, one constrained to have symmetry, the other with each tap adapting independently. Since the behavior of the adaptive Filters is dependent upon the characteristics of the signal on the filter taps, the authors explore differences in convergence among these two smoothing algorithms and the LMS predictor through this signal vector. By analyzing in detail this behavior for a sinusoid in white noise, they show that if the sinusoid is neither very low nor high in frequency then the constrained smoother has both better convergence and steady-state behavior than either the predictor or the unconstrained smoother while requiring one-half as many multiplications. >
