Clipped LMS/RLS Adaptive Algorithms: Analytical Evaluation and Performance Comparison with Low-Complexity Counterparts

Bekrani, Mehdi; Lotfizad, Mojtaba

doi:10.1007/s00034-014-9923-1

Cited by 7 publications

(6 citation statements)

References 19 publications

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“…Therefore, let us assume in the following that a different equalization factor, β k , is desired for each sensor in Figure 2, where the "LMS" block performs the MEFxLMS algorithm. This algorithm selection is not exclusive, so different, and generally more complex, alternative adaptive strategies could be explored, as the recently proposed Recursive Least Squares based Model Predictive Control (RLSMPC) [36] or noiserobust low complexity adaptive schemes like described in [37], [38]. The system needs to know all the pseudo-error noise signals and all the signals that result from filtering the in-phase and quadrature components of the reference signal through their corresponding estimated frequency response of the secondary paths, C jk .…”

Section: Multi-channel Ane For Single-frequency Noisementioning

confidence: 99%

Multi-Tone Active Noise Equalizer With Spatially Distributed User-Selected Profiles

Ferrer¹,

Diego²,

Hassani

et al. 2022

IEEE/ACM Trans. Audio Speech Lang. Process.

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In this work we propose a multi-channel active noise equalizer (ANE) that can deal with multi-frequency noise signals and assigns simultaneously different equalization gains to each frequency component at each monitoring sensor. For this purpose, we state a pseudo-error noise signal for each sensor of the ANE, which has to be cancelled out in order to get the desired equalization profiles. Firstly the optimal analytic solution for the ANE filters in the case of single-frequency noise is provided, and an adaptive algorithm based on the Least Mean Squared (LMS) is proposed for the same case. We also show that this adaptive strategy reaches the theoretical solution in steady state. Secondly, we state an equivalent approach for the case of multi-frequency noise based on two alternatives: a common pseudo-error signal at each sensor for all frequencies, and a different pseudo-error signal at each sensor for each frequency. The analytic and adaptive solutions for the ANE control filters have been developed for both pseudo-error alternatives. Finally, the ability of the proposed ANE to achieve simultaneously different user-selected noise profiles in different locations has been validated by their transfer functions and simulations.

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Section: Multi-channel Ane For Single-frequency Noisementioning

confidence: 99%

Multi-Tone Active Noise Equalizer With Spatially Distributed User-Selected Profiles

Ferrer¹,

Diego²,

Hassani

et al. 2022

IEEE/ACM Trans. Audio Speech Lang. Process.

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“…Recently, there have been many research attempts to improve computational efficiency for RLS [5]- [11], [38]. In [5], [6], a computationally efficient RLS algorithm was developed by extending the idea of Kronecker product decomposition proposed in [39].…”

Section: Low Complexity/precision For Rlsmentioning

confidence: 99%

“…In [11], a variation of RLS algorithm having computational complexity from N 2 /2 to 5N 2 /6 was presented, but the algorithm was not suitable for lower precision fixed point arithmetic due to the accumulation of rounding errors. In [38], the clipped LMS and RLS algorithms, that quantise the input signals to {−1, 0, 1}, were evaluated in terms of accuracy and computational complexity, compared to other low-complexity RLS algorithm counterparts such as the signed regressor RLS, the M Max tap-selection RLS, and the original RLS. Based on the evaluations, the optimal step sizes and forgetting factors for the clipped LMS and RLS that minimise the weight misalignment were derived.…”

Section: Low Complexity/precision For Rlsmentioning

confidence: 99%

Towards Lower Precision Adaptive Filters: Facts From Backward Error Analysis of RLS

Lee

Vandierendonck

2021

IEEE Trans. Signal Process.

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“…Inserting (26) into (24) and rearranging terms, and taking the weighted Euclidean norms on both sides, the energy conservation relation of the VC-VS-APMCCA can be established as follow: (27) where w(k + 1) 2 denotes the weighted squared norm w T (k + 1) w(k + 1). By combining the expressions (25) and (27) yields (k)e(k).…”

Section: Performance Analysismentioning

confidence: 99%

“…During the past two decades, several approaches were developed for performance improvement in the presence of impulsive noise [22]- [24]. The sign algorithm and clipped algorithm were proposed for stable performance when large outliers exist in the system [25], [26]. However, they suffer from slow convergence rate.…”

Section: Introductionmentioning

confidence: 99%

Affine Projection Algorithm by Employing Maximum Correntropy Criterion for System Identification of Mixed Noise

Wang

Han

2019

IEEE Access

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The adaptive algorithms have been widely studied in Gaussian environment. However, the impulsive noise and other non-Gaussian noise may largely deteriorate the performance of algorithm in practical applications. To address this problem, in this paper, we propose two novel adaptive algorithms for system identification problem with mixed noise scenarios. Both proposed algorithms are based on the framework of the affine projection (AP) algorithm. The first proposed algorithm, termed as VS-APMCCA, combines variable step-size (VS) strategy and maximum correntropy criterion (MCC) to obtain improved performance. For further performance improvement, the VC-VS-APMCCA is developed, which is based on the variable center (VC) scheme of MCC. The convergence analysis of the VC-VS-APMCCA is conduced. Finally, simulation results demonstrate the superior performance of the VS-APMCCA and VC-VS-APMCCA. INDEX TERMS Affine projection algorithm, variable step-size, maximum correntropy criterion (MCC), variable center (VC), mixed noise.

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Clipped LMS/RLS Adaptive Algorithms: Analytical Evaluation and Performance Comparison with Low-Complexity Counterparts

Cited by 7 publications

References 19 publications

Multi-Tone Active Noise Equalizer With Spatially Distributed User-Selected Profiles

Multi-Tone Active Noise Equalizer With Spatially Distributed User-Selected Profiles

Towards Lower Precision Adaptive Filters: Facts From Backward Error Analysis of RLS

Affine Projection Algorithm by Employing Maximum Correntropy Criterion for System Identification of Mixed Noise

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