Design of a Test for Detecting the Presence of Impulsive Noise

Oh, Hyungkook; Seo, Dongho; Nam, Haewoon

doi:10.3390/s20247135

Cited by 4 publications

(2 citation statements)

References 27 publications

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“…In [23], the complementary the presence of cumulative density function method is used to detect impulsive noise in PLC. In this approach, the Middleton Class A and symmetric-alpha-stable models are used to determine the weights.…”

Section: Previous Workmentioning

confidence: 99%

Low-Voltage PLC Noise Modelling

Chelangat

Afullo

2022

IRECAP

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This paper models the PLC impulsive noise using a linear superposition of univariate Gaussian distributions where the Bayes' theorem is used to find the posterior probabilities. The Gaussian mixture is formulated using discrete latent variables and modelled using two, three and four components in order to evaluate the effect of the number of components (Q). The parameters of the Gaussian mixture are then estimated using the maximum likelihood technique and the expectation-maximization algorithm. Regression analysis is proposed in order to solve the issue of singularity which is often present when the maximum likelihood approach is employed. The model is then validated through measurements where the impulsive noise is categorized into low, medium and highly impulsive depending on the amplitude of the indoor PLC noise. It is observed that as the number of components increases the performance of the Gaussian mixture model also increases as depicted by the correlation coefficient and RMSE. The χ 2 test indicates that the proposed model provides a better fit as the PLC noise amplitude increases. In addition, the shape of the impulsive noise PDF becomes more defined with higher Q values. A singularity case is also examined where the Gaussian mixture model also provides a good approximation of the measured data.

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Section: Previous Workmentioning

confidence: 99%

Low-Voltage PLC Noise Modelling

Chelangat

Afullo

2022

IRECAP

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“…However, in the complex field environment, the sensor is vulnerable to external interference, enemy attack or node failure. The bearing measurements may suffer impulse noise [ 10 , 11 , 12 , 13 ] and those outlier data can degrade the localization performance dramatically. Therefore, it is necessary to develop new estimators that are robust to impulsive noise.…”

Section: Introductionmentioning

confidence: 99%

A Total Lp-Norm Optimization for Bearing-Only Source Localization in Impulsive Noise with SαS Distribution

Luo

Xue

Rong

et al. 2021

Sensors

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This paper considers the problem of robust bearing-only source localization in impulsive noise with symmetric α-stable distribution based on the Lp-norm minimization criterion. The existing Iteratively Reweighted Pseudolinear Least-Squares (IRPLS) method can be used to solve the least LP-norm optimization problem. However, the IRPLS algorithm cannot reduce the bias attributed to the correlation between system matrices and noise vectors. To reduce this kind of bias, a Total Lp-norm Optimization (TLPO) method is proposed by minimizing the errors in all elements of system matrix and data vector based on the minimum dispersion criterion. Subsequently, an equivalent form of TLPO is obtained, and two algorithms are developed to solve the TLPO problem by using Iterative Generalized Eigenvalue Decomposition (IGED) and Generalized Lagrange Multiplier (GLM), respectively. Numerical examples demonstrate the performance advantage of the IGED and GLM algorithms over the IRPLS algorithm.

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