Sign normalised spline adaptive filtering algorithms against impulsive noise

Liu, Chang; Zhang, Zhi; Tang, Xiao

doi:10.1016/j.sigpro.2018.02.022

Cited by 54 publications

(20 citation statements)

References 12 publications

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“…In the impulsive noise-free background, Figs. 9 and 10 show the MSE learning curves of the SAF-LMS [3], SAF-NLMS [20], and the proposed SAF-RGM algorithm. For Fig.…”

Section: B Performance Resultsmentioning

confidence: 99%

“…where R x = E{x n x T n } represents the autocorrelation matrix of the input signal. Combining (18), (20), and 21yields…”

Section: A Mean Analysis Of (13)mentioning

confidence: 99%

“…In order to deal with this issue, the sign adaptive algorithm, whose weight vector is updated by using the l 1 norm optimization criterion, was introduced. Then, a series of variants of the sign algorithm were proposed, such as the sign normalized least mean square algorithm based on Wiener spline adaptive filter (SAF-SNLMS) [20], sign subband adaptive filter (SSAF) [21], and so on. Furthermore, to speed the convergence of the aforementioned sign algorithms, the variable step-size SAF-SNLMS and the variable step size sign subband adaptive filter (VSS-SSAF) [22] were introduced.…”

Section: Introductionmentioning

confidence: 99%

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Robust Geman-McClure Based Nonlinear Spline Adaptive Filter Against Impulsive Noise

Liu

2020

IEEE Access

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This paper first proposes nonlinear spline adaptive filter based on the robust Geman-McClure estimator (SAF-RGM). The proposed algorithm is obtained by minimizing the cost function relied on the Geman-McClure estimator. Since the Geman-McClure estimator can remove outliers with large amplitude from dataset, the proposed algorithm can obtain the excellent performance in the impulsive noise. Moreover, the mean and mean square behaviors of the SAF-RGM algorithm are analyzed. Simulations are conducted to confirm that the proposed SAF-RGM algorithm achieves better performance than the existing spline nonlinear adaptive filtering algorithms. Besides, simulation results validate the theoretical conclusions.

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“…In the impulsive noise-free background, Figs. 9 and 10 show the MSE learning curves of the SAF-LMS [3], SAF-NLMS [20], and the proposed SAF-RGM algorithm. For Fig.…”

Section: B Performance Resultsmentioning

confidence: 99%

“…where R x = E{x n x T n } represents the autocorrelation matrix of the input signal. Combining (18), (20), and 21yields…”

Section: A Mean Analysis Of (13)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Robust Geman-McClure Based Nonlinear Spline Adaptive Filter Against Impulsive Noise

Liu

2020

IEEE Access

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“…In practical applications, when the stress damage of the SLM forming parts reaches the plastic deformation level, which are no longer met the basic requirements of servicing conditions, it is not necessary to fit the acoustic-elastic curves at this stage. The linear part of acoustic-elastic curves in the a 1 and a 2 directions were fitted by the least square method [30] based on the criterion of a high correlation coefficient and small standard deviation [30,31]. Table 3.…”

Section: Calibration Of the Acoustic-elastic Coefficientmentioning

confidence: 99%

Study on the Measurement of Stress in the Surface of Selective Laser Melting Forming Parts Based on the Critical Refraction Longitudinal Wave

Yan

Pan

2019

Coatings

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Measurement and control of stress in the metal forming layer is the basic problem of selective laser melting (SLM) forming parts. The critical refraction longitudinal (LCR) wave method to test stress in metallic materials has been extensively studied. However, when testing of stress in selective laser melting (SLM) forming parts using this method, some deep-seated regularities of this technology are still not clear. In order to reveal the mechanism of the LCR wave method to measure stress in SLM forming parts, specimens made of 316 L stainless steel were manufactured using meander, stripe, and chessboard scanning strategies. Static load tensile test were applied to SLM forming specimens, with the purpose to demonstrate the scanning strategy has important effect on the LCR wave method to test stress in SLM forming parts. The regularity of the LCR wave velocity on stress is obtained in this study. The anisotropic microstructure of SLM forming parts has an unneglectable effect on the LCR wave stress test. The essential principle of anisotropic microstructure effecting the LCR wave velocity in SLM forming parts were revealed in the experiments. The results of the experiment provide a basis for non-destructive and reliable test of stress in SLM forming parts and other inhomogeneous materials.

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“…Various traditional modern signal processing methods, such as empirical mode decomposition (EMD) [6], wavelet transform [7,8], adaptive filter [9,10], Kalman filter [11,12], and mathematical morphology analysis [13,14], have been widely used in vibration signal analysis. Qin et al [5] proposed a novel M-band flexible wavelet transform for identifying the underlying fault features in measured signals.…”

Section: Introductionmentioning

confidence: 99%

A New Fault Diagnosis Method for Rotating Machinery Based on SCA-FastICA

Miao

Zhao

2020

Mathematical Problems in Engineering

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When the rotary machinery is running, the vibration signals measured with sensors are mixed with all vibration sources and contain very strong noises. It is difficult to separate mixed signals with conventional methods of signal processing, so there are difficulties in machine health monitoring and fault diagnosis. The principle and method of blind source separation were introduced, and it was pointed out that the blind source separation algorithm was invalid in strong pulse noise environment. In these environments, the vibration signals are first denoised with the synchronous cumulative average noise reduction (SCA) method, and the denoised signals were separated with the improved fast independent component analysis (FastICA) algorithm. The results of simulation test and rotor fault experiments demonstrate that the novel method can effectively extract fault features, certifying its superiority in comparison with previous methods. Therefore, it is likely to be useful and practical in the fault detection area, especially under the condition of strong noise and vibration interferences.

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Sign normalised spline adaptive filtering algorithms against impulsive noise

Cited by 54 publications

References 12 publications

Robust Geman-McClure Based Nonlinear Spline Adaptive Filter Against Impulsive Noise

Robust Geman-McClure Based Nonlinear Spline Adaptive Filter Against Impulsive Noise

Study on the Measurement of Stress in the Surface of Selective Laser Melting Forming Parts Based on the Critical Refraction Longitudinal Wave

A New Fault Diagnosis Method for Rotating Machinery Based on SCA-FastICA

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