Design and Implementation of an Improved Variable Step-Size NLMS-Based Algorithm for Acoustic Noise Cancellation

Salah, Mohamed; Dessouky, Mohamed; Abdelhamid, Bassant

doi:10.1007/s00034-021-01796-5

Cited by 11 publications

(3 citation statements)

References 29 publications

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“…, where the superscript T means transpose. Starting from the NLMS weight update recursion [14] : (5) In practice, it is necessary to regularize adaptive algorithms to prevent divisions by small numbers. This entails adding a positive constant to the denominator of the step size [15] .…”

Section: A Normalized Pi Algorithmmentioning

confidence: 99%

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FPGA Implementation of Variable Step Power Inversion Array for BeiDou Receiver

Zhuoya,

Shuyan,

Yalun

et al. 2023

IEEE Access

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Given the susceptibility of satellite signals to intentional or unintentional interference; it is imperative to incorporate hardware-based navigation anti-jamming algorithms. Currently, spatial domain filtering is widely applied. While numerous variable step size (VSS) algorithms were used to improve the performance, most of these are either computationally complex or depend on many parameters which are hard to tune manually. Some algorithms might improve one index but worsen overall performance, making them unreliable in practical scenarios. To address these challenges, we propose a novel variable step power inversion (PI) that involves just one adaptive parameter. By establishing a new non-linear function model between step size and system output, the parameter can be adapted based on the output. Meanwhile, a variable step PI that can be easily implemented by field programmable gate array (FPGA) are proposed, thereby achieving a balance between accuracy and iteration speed. The proposed algorithm can converge within 10 iterations, effectively suppressing individual interference to below -300dB. In comparison to existing algorithms, the proposed algorithm is characterized by swift convergence, high convergence accuracy, low complexity, and robust anti-jamming capabilities. Finally, FPGA hardware testing are conducted, and the experimental and simulation results are in good agreement, affirming anti-jamming capabilities.INDEX TERMS power inversion array; variable step size (VSS); navigation anti-jamming; FPGA

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Section: A Normalized Pi Algorithmmentioning

confidence: 99%

“…During the Russo-Ukrainian conflict, Russia employed electronic warfare system to execute high-power suppression jamming, resulting in the failure of Ukraine's GPS system. Thus, recent research has focused on not only the performance measures of the algorithm but also on FPGA implementations [3][4][5] .…”

Section: Introductionmentioning

confidence: 99%

FPGA Implementation of Variable Step Power Inversion Array for BeiDou Receiver

Zhuoya,

Shuyan,

Yalun

et al. 2023

IEEE Access

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“…Another factor impacting RUL prediction accuracy is the signal noise, which arises from complex operating conditions and signal transmission. Sensor signal denoising [15] is also a traditional challenge in signal processing. In engineering practice, strategies such as filters and feature extraction are usually used for noise reduction.…”

Section: Introductionmentioning

confidence: 99%

DRLSTM-DA: a novel aeroengine remaining useful life prediction method based on deep optimization of multiple sensors information

Suo,

Fu,

Lin

et al. 2024

Meas. Sci. Technol.

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One of the crucial tasks in Remaining useful life prediction is to effectively extract key sensor information from numerous sensor signals. In this article, a novel dual-attention enhanced deep residual LSTM (DRLSTM-DA) is developed to deeply optimize multidimensional sensor signals. First, two LSTM layers are designed to compress and reconstruct representative degradation information from input multidimensional time series data, to generate a new feature space. Second, a novel channel adaptive soft threshold module is designed to assign different weights according to the importance of different sensor information, and simultaneously eliminate the noise information in the signal. Thirdly, a temporal attention mechanism is designed to automatically highlight moments containing important decay information while suppressing unimportant moments. Finally, compared with other SOTA methods, our architecture achieves RMSE of 11.55, 13.74, 11.25, and 14.19 on four sub-datasets of the C-MAPSS dataset, with scores of 234.24, 465.49, 202.23, and 537.66, respectively. Meanwhile, in real aeroengine operation dataset, our architecture achieved the smallest RMSE (8.62). These results all validate the good predictive performance of our model.

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