Estimation-based synthesis of H∞-optimal adaptive FIR filters for filtered-LMS problems

Sayyarrodsari, B.; How, Jonathan P.; Hassibi, Babak; Carrier, Alain

doi:10.1109/78.890358

Cited by 19 publications

(12 citation statements)

References 18 publications

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“…The objective function for this new augmented problem is the same as in Eq. (7). Looking at Equation (9), it is clear that the estimation problem is nonlinear in the uncertain parameter vector p, and therefore no closed form solution to the problem is known.…”

Section: Problem Formulationmentioning

confidence: 98%

“…Conditions for optimality of this solution (e.g. persistent excitation and its manifestation for this problem) can be found in [6,7].…”

Section: Filtering Solutionmentioning

confidence: 99%

“…This approach uses an estimation interpretation to replace the adaptive control problem with an H, estimation problem that is easier to solve. This approach is an extension to the work in [6,7](which itself is an extension to the work in [8] and 191). The goal in this paper is to show that the estimation-based approach provides a convenient framework in which parameter estimation for the secondary path can be made an integral part of the overall adaptive control algorithm.…”

Section: Introductionmentioning

confidence: 97%

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Estimation-based approach to simultaneous adaptive control and parameter estimation in filtered-LMS problems

Alighanbari

Sayarrodsari²,

Homaifar

2001

Proceedings of the 2001 American Control Conference. (Cat. No.01CH37148)

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This paper presents an estimation-based approach to the adaptive filtering (control) problem in the Filtered-LMS scenarios with unknown parameters in the secondary path. The approach discussed here produces a robust estimate for the unknown system parameters while fulfilling adaptive control objectives. The control objective is to limit the worst-case energy gain from exogenous disturbances, initial condition uncertainty, and parameter estimation error to the cancellation error at the output of the secondary path. Simulation results on a typical active noise cancellation problem are presented to demonstrate the main features of the proposed approach.

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Section: Problem Formulationmentioning

confidence: 98%

“…Conditions for optimality of this solution (e.g. persistent excitation and its manifestation for this problem) can be found in [6,7].…”

Section: Filtering Solutionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 97%

See 1 more Smart Citation

Estimation-based approach to simultaneous adaptive control and parameter estimation in filtered-LMS problems

Alighanbari

Sayarrodsari²,

Homaifar

2001

Proceedings of the 2001 American Control Conference. (Cat. No.01CH37148)

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“…Since it is hard to characterize the uncertainty under the complexity of acoustic dynamics, the best strategy might be just to assume that the disturbance is bounded. This naturally leads to the formulation of the adaptive f H filter approach [9]- [10] which makes no further assumption on the disturbances. To apply the f H adaptive filter algorithm, the linear model of equation (6) can be considered as:…”

Section: Multi-channel Adaptive Algorithmmentioning

confidence: 99%

Speech enhancement using transfer function ratio beamformer and matched filter array

Yang

2009

2009 International Conference on Information and Automation

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Human machine interface for speech processing often suffers from interferences which contain nonstationary signal, stationary signal and reverberation. In this paper, we consider two speech sources in the reverberant and noisy environment and we develop a speech enhancement approach to extract desired speech signal from the corrupt observations. The proposed method uses transfer function ratio beamformer, f H adaptive filter algorithm and matched filter array to perform speech enhancement as well as dereverberation at two stages. The desired speech is extracted at the first stage and the extracted speech which contains reverberation is dereverberated at the second stage.

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“…Usually this assumption is not satisfied, but the error due to imposing this assumption can often be neglected. Assuming that the noise signals v s ðkÞ and v m ðkÞ are uncorrelated with the disturbance reference signal rðkÞ; it can be verified easily that the residual signal is minimized if the feedforward controller # W k ðq À1 Þ equals W o ðq À1 Þ: In this way, the feedforward control problem is reformulated in an estimation context [6].…”

Section: The State Estimation Problemmentioning

confidence: 99%

A fast‐array Kalman filter solution to active noise control

Fraanje

Sayed

Verhaegen

et al. 2004

Adaptive Control & Signal

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A Kalman filter solution to active control and its fast-array implementation are provided. The adaptive control problem is formulated as a state-estimation problem and no interchanging of the adaptive filter and the secondary-path is imposed. Moreover, no estimate of the disturbance signal is needed, and we exploit the structure in the state-space matrices to derive a fast-array implementation. A minimum variance estimate of the controller coefficients and the secondary path state is obtained. When there is no uncertainty in the secondary path, state equivalence with the modified filtered-RLS algorithm is proven. Using exponential forgetting, the analysis shows that in the generation of the filtered reference signal in the modified filtered-RLS, exponential forgetting should be incorporated too. Simulations show the superiority in convergence of the fast-array Kalman algorithm over the fast-array modified filtered-RLS algorithm.

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Estimation-based synthesis of H∞-optimal adaptive FIR filters for filtered-LMS problems

Cited by 19 publications

References 18 publications

Estimation-based approach to simultaneous adaptive control and parameter estimation in filtered-LMS problems

Estimation-based approach to simultaneous adaptive control and parameter estimation in filtered-LMS problems

Speech enhancement using transfer function ratio beamformer and matched filter array

A fast‐array Kalman filter solution to active noise control

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