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Pattern Recognition and Image Analysis

Erschienen in:

01.07.2020 | APPLIED PROBLEMS

Efficiency of the Method for Detecting Normal Mixture Signals with Pre-Estimated Gaussian Mixture Noise

verfasst von: A. K. Gorshenin, A. A. Shcherbinina

Erschienen in: Pattern Recognition and Image Analysis | Ausgabe 3/2020

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Abstract

The paper discusses the effectiveness of the method for determining the parameters of the useful signal in the sliding window mode, provided that it is possible to obtain preliminary estimates for the noise distribution. For the statistical experiment, \(24\) samples had been generated with different ratios between the signal and noise parameters. Implementation of the computational procedures for the adaptive method in the Python programming language is proposed. For the test samples, it is demonstrated that the magnitude of the error in evaluating the parameters in the vast majority of cases does not exceed value 1 (in terms of the standard RMSE metrics). In addition, an effective two-pass method for detecting the moment of the appearance of a meaningful signal in the noisy data is proposed. The results of its operation are also demonstrated on the example of the mentioned test samples.

Keywords:

finite normal mixtures, method of moving separation of mixtures, detection, signal, noise, EM algorithm, computational algorithm

Vorheriger Artikel Two-Dimensional Filtering Method Using Systems of Local Model Functions for Muonogram Analysis

Nächster Artikel Solution of the Problem of Classification of Hydroacoustic Signals Based on Harmonious Wavelets and Machine Learning

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S. Márquez-Figueroa, Y. S. Shmaliy, and O. Ibarra-Manzano, “Optimal extraction of EMG signal envelope and artifacts removal assuming colored measurement noise,” Biomed. Signal Process. Control 57, Article 101679 (2020).CrossRef

H. Almgren, F. Van de Steen, A. Razi, K. Friston, and D. Marinazzo, “The effect of global signal regression on DCM estimates of noise and effective connectivity from resting state fMRI,” NeuroImage 208, Article 116435 (2020).CrossRef

H. Asadi and B. Seyfe, “Signal enumeration in Gaussian and non-Gaussian noise using entropy estimation of eigenvalues,” Digital Signal Process. 78, 163–174 (2018).CrossRef

M. C. Ilter, H. U. Sokun, H. Yanikomeroglu, R. Wichman, and J. Hämäläinen, “The joint impact of fading severity, irregular constellation, and non-Gaussian noise on signal space diversity-based relaying networks,” IEEE Access 7, 116162–116171 (2019).CrossRef

J. Guo, H. Zhang, D. Zhen, Z. Shi, F. Gu, and A. D. Ball, “An enhanced modulation signal bispectrum analysis for bearing fault detection based on non-Gaussian noise suppression,” Meas. 151, Article 107240 (2020).

Y. Li, Z. Li, K. Wei, W. Xiong, J. Yu, and B. Qi, “Noise estimation for image sensor based on local entropy and median absolute deviation,” Sensors 19 (2), Article 339 (2019).CrossRef

A. K. Gorshenin, “Data noising by finite normal and gamma mixtures with application to the problem of rounded observations,” Inform. Primen. (Inf. Appl.) 12 (3), 28–34 (2018) [in Russian].

A. K. Gorshenin, “Adaptive detection of normal mixture signals with pre-estimated Gaussian mixture noise,” Pattern Recogn. Image Anal. 29 (3), 377–383 (2019).CrossRef

V. Yu. Korolev, Probabilistic and Statistical Methods of Decomposition of Volatility of Chaotic Processes (Mosk. Gos. Univ., Moscow, 2011) [in Russian].MATH

10.

E. S. Page, “On problems in which a change in a parameter occurs at an unknown point,” Biometrika 44 (1–2), 248–252 (1957).CrossRef

11.

D. Picard, “Testing and estimating change-points in time series,” Adv. Appl. Prob. 17 (4), 841–867 (1985).MathSciNetCrossRef

12.

J. Bloemer, S. Brauer, K. Bujna, and D. Kuntze, “How well do SEM algorithms imitate EM algorithms? A non-asymptotic analysis for mixture models,” Adv. Data Anal. Classif. 14 (1), 147–173 (2020).MathSciNetCrossRef

13.

Y. Tang, “Beyond EM: A faster Bayesian linear regression algorithm without matrix inversions,” Neurocomput. 378, 435–440 (2020).CrossRef

14.

Y. Tuac, Y. Guney, and O. Arslan, “Parameter estimation of regression model with AR(p) error terms based on skew distributions with EM algorithm,” Soft Comput. 24 (5), 3309–3330 (2020).CrossRef

15.

C. Liu, H.-C. Li, K. Fu, F. Zhang, M. Datcu, and W. J. Emery, “Bayesian estimation of generalized Gamma mixture model based on variational EM algorithm,” Pattern Recogn. 87, 269–284 (2019).CrossRef

16.

L. Yu, T. Yang, and A. B. Chan, “Density-preserving hierarchical EM algorithm: Simplifying Gaussian mixture models for approximate inference,” IEEE Trans. Pattern Anal. Mach. Intell. 41 (6), 1323–1337 (2019).CrossRef

17.

D. Wu and J. Ma, “An effective EM algorithm for mixtures of Gaussian processes via the MCMC sampling and approximation,” Neurocomput. 331, 366–374 (2019).CrossRef

18.

Y. Kojima, H. Matsumoto, and H. Kiryu, “Estimation of population genetic parameters using an EM algorithm and sequence data from experimental evolution populations,” Bioinf. 36 (1), 221–231 (2020).CrossRef

19.

A. K. Gorshenin, V. Yu. Korolev, and A. M. Tursunbayev, “Median modifications of the EM-algorithm for separation of mixtures of probability distributions and their applications to the decomposition of volatility of financial indexes,” J. Math. Sci. 227 (2), 176–195 (2018).MathSciNetCrossRef

20.

A. Gorshenin, V. Korolev, V. Kuzmin, and A. Zeifman, “Coordinate-wise versions of the grid method for the analysis of intensities of non-stationary information flows by moving separation of mixtures of gamma-distribution,” in Proc. 27th European Conference on Modelling and Simulation (ECMS 2013) (Ålesund, Norway, 2013), pp. 565–568.

21.

A. K. Gorshenin, “On implementation of EM-type algorithms in the stochastic models for a matrix computing on GPU,” AIP Conf. Proc. 1648, Article 250008 (2015).CrossRef

22.

G. M. Batanov, V. D. Borzosekov, A. K. Gorshenin, N. K. Kharchev, V. Yu. Korolev, and K. A. Sarskyan. “Evolution of statistical properties of microturbulence during transient process under electron cyclotron resonance heating of the L-2M stellarator plasma,” Plasma Phys. Controlled Fusion 61 (7), Article 075006 (2019).

Titel: Efficiency of the Method for Detecting Normal Mixture Signals with Pre-Estimated Gaussian Mixture Noise
verfasst von: A. K. Gorshenin
A. A. Shcherbinina
Publikationsdatum: 01.07.2020
Verlag: Pleiades Publishing
Erschienen in: Pattern Recognition and Image Analysis / Ausgabe 3/2020
Print ISSN: 1054-6618
Elektronische ISSN: 1555-6212
DOI: https://doi.org/10.1134/S1054661820030074

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