Distributed Affine Projection Algorithm Over Acoustically Coupled Sensor Networks

Ferrer, Miguel; González, Alberto; Diego, María de; Piñero, Gema

doi:10.1109/tsp.2017.2742987

Cited by 34 publications

(22 citation statements)

References 31 publications

(55 reference statements)

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“…For arbitrary matrices A, B, C of compatible dimensions, vec(ABC) = (C T ⊗ A)vec(B) [1], [44]. Applying this operation to (24) gives rise to…”

Section: Define the Augmented Weight Error Vectorw(k)mentioning

confidence: 99%

“…In [21], a novel analysis using the propagation model of the error covariance was put forward, which considers the cross-correlation between the weight error vector and the noise vector. Following the propagation model of the error covariance, the periodic APA (P-APA) [23] and the distributed filtered-x APA (DFxAPA) [24] were analyzed in detail.…”

Section: Introductionmentioning

confidence: 99%

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Variable Step-Size Widely Linear Complex-Valued Affine Projection Algorithm and Performance Analysis

Shi

Zhao

Zakharov

et al. 2020

IEEE Trans. Signal Process.

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In this paper, a variable step-size widely linear complex-valued affine projection algorithm (VSS-WLCAPA) is proposed for processing noncircular signals. The variable stepsize (VSS) is derived by minimizing the power of the augmented noise-free a posteriori error vector, which speeds up the convergence and reduces the steady-state misalignment. By exploiting the evolution of the covariance matrix of the weight error vector, we provide insight into the theoretical behavior of the VSS-WLCAPA algorithm. In the analysis, we take into account the dependency between the weight error vector and the noise vector, which is useful for accuracy of the theoretical prediction. To evaluate the mean step-size, the probability density function of the magnitude of the error is derived by employing polar coordinate transformation. Moreover, a special case when the projection order reduces to one is analysed in detail. The presented theoretical analysis is different from existing methodologies for analyzing affine projection algorithms due to the use of the Kronecker product. Simulation results for system identification scenarios demonstrate the merits of the proposed algorithm and verify the accuracy of the theoretical analysis. Wind prediction experiments support the superiority of the proposed VSS-WLCAPA as well.

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“…For arbitrary matrices A, B, C of compatible dimensions, vec(ABC) = (C T ⊗ A)vec(B) [1], [44]. Applying this operation to (24) gives rise to…”

Section: Define the Augmented Weight Error Vectorw(k)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Variable Step-Size Widely Linear Complex-Valued Affine Projection Algorithm and Performance Analysis

Shi

Zhao

Zakharov

et al. 2020

IEEE Trans. Signal Process.

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“…Recently, and motivated by the good trade-off between convergence speed and computational cost of the AP algorithms, two approaches that address the distribution of the AP processing over ASNs have been presented [15], [16]. However, they are approximated distributed versions of the centralized multichannel FxAP (MFxAP) algorithm [31]- [34] and, from their analysis, it is not assured that they can obtain the same performance as the MFxAP.…”

Section: Introductionmentioning

confidence: 99%

“…Three different strategies to improve the computational efficiency of EFxAP are proposed in Section IV whereas Section V studies their corresponding computational costs. Section VI evaluates the performance of the EFxAP compared to the DMEFxLMS [14] and the two approximated distributed filtered-x AP algorithms in [15], [16]. The main conclusions are summarized in Section VII.…”

Section: Introductionmentioning

confidence: 99%

Affine Projection Algorithm Over Acoustic Sensor Networks for Active Noise Control

Ferrer

Diego

Piñero

et al. 2021

IEEE/ACM Trans. Audio Speech Lang. Process.

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“…Alternatively, active control methods can be applied, which are not limited by such restrictions. [1][2][3][4] In particular, active structural acoustic control (ASAC) has been extensively used to reduce noise passing through individual barriers. 5 Different control strategies have been examined over the years.…”

Section: Introductionmentioning

confidence: 99%

Feedforward control of double-panel casing for active reduction of device noise

Wrona

Pawełczyk

2018

Journal of Low Frequency Noise, Vibration and Active Control

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The active casing approach is a technique to reduce noise emission of devices and machinery by controlling vibrations of their casings. This method was successfully validated by the authors for a single-panel casing in previous publications. However, if even higher noise reduction is required, a dedicated double-panel structure can be employed. In this paper, the sound insulation efficiency of a double-panel active casing is evaluated and compared with a single-panel structure by performing a series of laboratory experiments. The least mean squares algorithm is used to adaptively update control filter parameters. A low-frequency noise in the range up to 500 Hz is considered. Advantages and limits of the proposed approach are pointed out and discussed, and conclusions for future research are given.

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Distributed Affine Projection Algorithm Over Acoustically Coupled Sensor Networks

Cited by 34 publications

References 31 publications

Variable Step-Size Widely Linear Complex-Valued Affine Projection Algorithm and Performance Analysis

Variable Step-Size Widely Linear Complex-Valued Affine Projection Algorithm and Performance Analysis

Affine Projection Algorithm Over Acoustic Sensor Networks for Active Noise Control

Feedforward control of double-panel casing for active reduction of device noise

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