A recursive least square algorithm for active control of mixed noise

Wu, Lifu; Qiu, Xiaojun; Burnett, Ian; Guo, Yecai

doi:10.1016/j.jsv.2014.11.002

Cited by 36 publications

(16 citation statements)

References 19 publications

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“…This may be the reason that recent work on ANC of impulsive sources (being modeled as stable process) does not include the theoretical analysis, and in fact, the simulations have been used as a major tool to demonstrate the effectiveness of the proposal (see, for example, [7,8,11]). The interested reader may also look into the recent works on ANC [25][26][27][28][29][30][31]. Though simulations do not prove, they do demonstrate the effectiveness.…”

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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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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“…The FxGAL algorithm using the lattice form to orthogonalize correlated inputs shows better performance and lower computational complexity than the FxAP algorithm. However, as the regression coefficients are updated by a stochastic gradient approach such as the normalized least mean square (NLMS) algorithm, the performance of the FxGAL algorithm cannot reach that of the filtered-input recursive least-squares (FxRLS) algorithm based on a least-square estimation [18]- [21]. Although the FxRLS algorithm shows the best performance among all aforementioned algorithms, it has not been applied to ANC systems owing to its high computational complexity of O(M 2 ) operations per iteration [22], where M is the tap length of the filter.…”

Section: Introductionmentioning

confidence: 99%

Recursive Least-Squares Lattice Algorithm Combined With Secondary-Path Innovation and Lattice-Order Decision Algorithms for Active Noise Control

Kim

Park

2020

IEEE Access

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This paper proposes a new active noise control (ANC) system based on a recursive least-squares lattice (RLSL) algorithm by designing secondary-path innovation (SPI) and lattice-order decision (LOD) algorithms. The SPI algorithm associated with the power spectral factorization of the secondary path is designed to apply the RLSL algorithm to the ANC system without the filtered-input structure. The SPI algorithm whitens the error microphone signals into the virtual error signals just ahead of the secondary path to construct the virtual desired signals corresponding to the outputs of the lattice filter. The LOD algorithm is designed to operate the ANC system without knowing the tap length of the optimal finite impulse response (FIR) filter. The decision algorithm using the initial and final estimation errors of the lattice filter estimates the tap length of the optimal FIR filter by increasing the lattice order one-by-one until the final estimation error reaches below a threshold value. Even in environments where the tap length of the optimal FIR filter is unknown, the proposed ANC system with low computational complexity shows almost the same performance as the ANC system based on a filtered-input recursive least squares algorithm.

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“…Similar to the LMS-based algorithms [21,22], the VFxMCC algorithm uses the gradient descent theory to update the weight vector. Moreover, the weight coefficient updating mode of VFxRMC algorithm is parallel to the recursive least square (RLS) based algorithms [23][24][25]. In order to further improve algorithm performance, we use the VFxRMC algorithm to update the 1st-order SOV filter coefficient and the 2nd-order SOV filter coefficient is updated by VFxMCC algorithm, which is called the hybrid (HVFx-RMC-MCC) algorithm.…”

Section: Introductionmentioning

confidence: 99%

An efficient algorithm for nonlinear active noise control of impulsive noise

2019

Applied Acoustics

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Nonlinear active noise control (NANC) systems employing Volterra filter suffer from the stability issues in the presence of impulsive noise. To solve this problem, we combine the second-order Volterra (SOV) filter and maximum correntropy criterion (MCC) in this paper. The Volterra filter-x maximum correntropy criterion (VFxMCC) algorithm and Volterra filter-x recursive maximum correntropy (VFxRMC) algorithm are applied to reduce the impulsive noise of NANC. We find that VFxMCC algorithm has a low computational complexity and VFxRMC algorithm converges fast. In order to extract their advantages, we further propose a hybrid algorithm based on the VFxMCC and VFxRMC algorithms. In addition, the normalize step-size version of VFxMCC (VFxnMCC) algorithm is developed to improve the robustness and performance. Meanwhile, we adaptively adjust the kernel size of MCC online based on the sample variance of reference signal to improve the performance of the proposed algorithms. Simulation results in the context of nonlinear active impulsive noise control demonstrate that the proposed algorithms achieve much better performance than the existing algorithms in various noise environments.

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A recursive least square algorithm for active control of mixed noise

Cited by 36 publications

References 19 publications

Robust adaptive algorithm for active control of impulsive noise

Robust adaptive algorithm for active control of impulsive noise

Recursive Least-Squares Lattice Algorithm Combined With Secondary-Path Innovation and Lattice-Order Decision Algorithms for Active Noise Control

An efficient algorithm for nonlinear active noise control of impulsive noise

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