Theoretical convergence analysis of FxLMS algorithm

The rapid growth of the industry has a major effect on the environmental noise pollution, and it ranks second to the air pollution that adversely affects the human health. Passive noise control techniques are impractical and very expensive for lowfrequency noises. To solve such acoustic problem active noise control has been studied since early 20th century. Active noise control is based on the principle of superposition: i.e., it mitigates the unwanted noise by generating an anti-noise having the same amplitude but opposite in phase. In this paper, we present the physical classification of the existing literature on the basis of both noise source and quiet zone characteristics. Examples are point to point, point to zone, zone to point and zone to zone. We focus on the developing trends of active noise control in the last decade and discuss latest add-on features and multi-channel active acoustic shielding for open windows.

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“…The analyses of both perfect and imperfect secondary path estimation of feedforward active noise control system are presented in [24][25][26].…”

Section: Feedforward Ancmentioning

confidence: 99%

Physical Classification and Recent Developments of Active Noise Control Systems

Munir

Abdulla

Ardekani

et al. 2017

Proceedings of the 9th International Conference on Signal Processing Systems

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“…Various attempts on derivation of theoretical convergence analysis for the FxLMS algorithm have been made with different simplified assumptions on inputs being single or multitonal, stationary or purely white, and secondary path being pure delay or moving average model, etc. [22,23].…”

Section: Performance Analysis and Computational Complexitymentioning

confidence: 99%

Robust adaptive algorithm for active control of impulsive noise

Mirza

Zeb

Sheikh

2016

EURASIP J. Adv. Signal Process.

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Active noise control (ANC) systems employing adaptive filters suffer from stability issues in the presence of impulsive noise. To overcome this limitation, new methods must be investigated. In this paper, we propose the filtered-x state-space recursive least square (FxSSRLS), an SSRLS-based practical and adaptive algorithm for ANC. Computer simulations are executed to verify the enhanced performance of the FxSSRLS algorithm. Symmetric α-stable (SαS) distributions are used to model impulsive noise. The results show that the proposed FxSSRLS algorithm is more robust in eliminating high-peaked impulses than the recently reported algorithms for ANC applications. Moreover, the suggested solution exhibits better stability and faster convergence, without jeopardizing the performance of the proposed solution in terms of residual noise suppression in the presence of impulses.

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“…There have been several contributions in modelling and analysis of the FxLMS adaptation process [3][4][5][6][7][8][9][10][11][12]. However, only a few have intended to find closed-form mathematical expressions for formulating this process.…”

Section: Introductionmentioning

confidence: 99%

“…This investigation resulted in a set of closed-form mathematical expressions for formulating behaviours of the FxLMS adaptation process. The results have been published in a series of research papers [9][10][11][12]. In [9], basic closed-form expressions for formulating behaviours of the FxLMS adaptation process with a general secondary path were derived.…”

Section: Introductionmentioning

confidence: 99%

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Stochastic modelling and analysis of filtered‐x least‐mean‐square adaptation algorithm

Ardekani

Abdulla

2013

IET signal process.

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This study represents a stochastic model for the adaptation process performed on adaptive control systems by the filtered-x least-mean-square (FxLMS) algorithm. The main distinction of this model is that it is derived without using conventional simplifying assumptions regarding the physical plant to be controlled. This model is then used to derive a set of closed-form mathematical expressions for formulating steady-state performance, stability condition and learning rate of the FxLMS adaptation process. These expressions are the most general expressions, which have been proposed so far. It is shown that some previously derived expressions can be obtained from the proposed expressions as special and simplified cases. In addition to computer simulations, different experiments with a real-time control setup confirm the validity of the theoretical findings.

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Theoretical convergence analysis of FxLMS algorithm

Cited by 98 publications

References 10 publications

Physical Classification and Recent Developments of Active Noise Control Systems

Physical Classification and Recent Developments of Active Noise Control Systems

Robust adaptive algorithm for active control of impulsive noise

Stochastic modelling and analysis of filtered‐x least‐mean‐square adaptation algorithm

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