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Intensity Estimation of Noise-Like Signal in Presence of Uncorrelated Pulse Interferences

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Abstract

The effective filtering of noisy signals is one of the topical and open problems of the processing of noisy signals characterized by the presence of pulse interferences. A robust approach to intensity estimation of noise-like signal in the presence of additive uncorrelated pulse interferences has been proposed. The presence of additive uncorrelated pulse interferences leads to an increase of dispersion of registered signal at separate sections with pulse interferences. The robustness of intensity estimation is achieved by reducing the influence of sections with pulse interferences. A variety of nonlinear filtering methods has been developed that are based on detecting the intensity using lower envelope: two-parameter recursive filter, dilation, limiting the derivative and order statistics. The numerical simulation was used to perform their comparison with the known most common methods. The numerical simulation confirmed the efficiency of the approach proposed for estimating the intensity of noise-like signal in the presence of additive uncorrelated pulse interferences. The developed techniques can be applied for signal processing in means of communications, measurement instrumentation, radio astronomy, and also for image processing.

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References

  1. A. Chakrabarty, “Large deviations for truncated heavy-tailed random variables: a boundary case,” Indian J. Pure Appl. Math. 48, No. 4, 671 (2017). DOI: 10.1007/s13226-017-0250-7.

    Article  MathSciNet  MATH  Google Scholar 

  2. V. Nayar, I. Kampouris, S. Sivitos, “Outliers: The dangers of not being one of the pack,” J. Investing 26, No. 4, 165 (2017). DOI: 10.3905/joi.2017.26.4.165.

    Article  Google Scholar 

  3. J. Kim, S. Lee, “A convenient approach for penalty parameter selection in robust lasso regression,” Commun. Statistical Applications Methods 24, No. 6, 651 (2017). DOI: 10.29220/CSAM.2017.24.6.651.

    Article  Google Scholar 

  4. A. C. Atkinson, A. Corbellini, M. Riani, “Robust Bayesian regression with the forward search: theory and data analysis,” TEST 26, No. 4, 869 (2017). DOI: 10.1007/s11749-017-0542-6.

    Article  MathSciNet  MATH  Google Scholar 

  5. F. J. Duque-Pintor, M. J. Fernandez-Gomez, A. Troncoso, F. Martinez-Alvarez, “A new methodology based on imbalanced classification for predicting outliers in electricity demand time series,” Energies 9, No. 9, 752 (2016). DOI: 10.3390/en9090752.

    Article  Google Scholar 

  6. V. B. Goryainov, E. R. Goryainova, “The influence of anomalous observations on the least squares estimate of the parameter of the autoregressive equation with random coefficient,” Vestnik MGTU im. Baumana. Ser. Natural Sci., No. 2, 16 (2016). DOI: 10.18698/1812-3368-2016-2-16-24.

    Google Scholar 

  7. G. Shevlyakov, N. Lyubomishchenko, P. A. Smirnov, “A few remarks on robust estimation of power spectra,” Austrian J. Statistics 43, No. 4, 237 (2014). DOI: 10.17713/ajs.v43i4.42.

    Article  Google Scholar 

  8. R. J. Kosarevych, B. P. Rusyn, V. V. Korniy, T. I. Kerod, “Image segmentation based on the evaluation of the tendency of image elements to form clusters with the help of point field characteristics,” Cybernetics Systems Analysis 51, No. 5, 704 (2015). DOI: 10.1007/s10559-015-9762-5.

    Article  MathSciNet  Google Scholar 

  9. B. Rusyn, O. Lutsyk, Y. Lysak, A. Lukenyuk, L. Pohreliuk, “Lossless image compression in the remote sensing applications,” Proc. of 2016 IEEE First Int. Conf. on Data Stream Mining & Processing, DSMP, 23–27 Aug. 2016, Lviv, Ukraine (IEEE, 2016), pp. 195–198. DOI: 10.1109/DSMP.2016.7583539.

    Google Scholar 

  10. I. Paliy, A. Sachenko, Y. Kurylyak, O. Boumbarov, S. Sokolov, “Combined approach to face detection for biometric identification systems,” Proc. of 5th IEEE Int. Workshop on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications, 21–23 Sept. 2009, Rende, Italy (IEEE, 2009), pp. 434–439. DOI: 10.1109/IDAACS.2009.5342946.

    Google Scholar 

  11. R. A. Fisher, “On the mathematical foundations of theoretical statistics,” Phil. Trans. R. Soc. A 222, 594 (1922). DOI: 10.1098/rsta.1922.0009.

    Article  Google Scholar 

  12. P. J. Bickel, E. L. Lehmann, “Descriptive statistics for nonparametric models. III. Dispersion,” The Annals Statistics 4, No. 6, 1139 (1976). URI: https://www.jstor.org/stable/2958585.

    Article  MathSciNet  MATH  Google Scholar 

  13. S. M. Stigler, “The changing history of robustness,” The Am. Statistician 64, No. 4, 277 (2010). DOI: 10.1198/tast.2010.10159.

    Article  MathSciNet  Google Scholar 

  14. A. Atkinson, M. Riani, “Introduction to Robust Statistics,” Proc. of 8th Int. Conf. of the ERCIM WG on Computational and Methodological Statistics, 12–14 Dec. 2015, Senate House, UK (2015). URI: http://cmstatistics.org/CMStatistics2015/docs/WinterCourseAR_Regression.pdf?20180201194816.

    Google Scholar 

  15. N. M. Neykov, “Robust statistical modelling through trimming,” PhD Dissertation. Sofia (2016).

    Google Scholar 

  16. M. Koller, M. Machler, “Definitions of ψ-functions available in robustbase,” The Comprehensive R Archive Network (2017). URI: https://cran.r-project.org/web/packages/robustbase/vignettes/psi_functions.pdf.

    Google Scholar 

  17. C. Croux, C. Dehon, “Robust estimation of location and scale,” in El-Shaarawi, A. H.; Piegorsch, W. W. (eds.) Encyclopedia of Environmetrics (John Wiley & Sons Ltd, Chichester, UK, 2013).

    Google Scholar 

  18. Christophe Leys, Christophe Ley, Olivier Klein, Philippe Bernard, Laurent Licataa, “Detecting outliers: Do not use standard deviation around the mean, use absolute deviation around the median,” J. Experimental Social Psychology 49, No. 4, 764 (2013). DOI: 10.1016/j.jesp.2013.03.013.

    Article  Google Scholar 

  19. D. E. Tyler, “A short course on robust statistics,” The State University of New Jersey. URI: http://www.rci.rutgers.edu/~dtyler/ShortCourse.pdf.

  20. M. A. Gandhi, L. Mili, “Robust Kalman filter based on a generalized maximum-likelihood-type estimator,” IEEE Trans. Signal Processing 58, No. 5, 2509 (2010). DOI: 10.1109/TSP.2009.2039731.

    Article  MathSciNet  MATH  Google Scholar 

  21. D. I. Lekhovytskiy, “Adaptive lattice filters for systems of space-time processing of non-stationary Gaussian processes,” Radioelectron. Commun. Syst. 61, No. 11, 477 (2018). DOI: 10.3103/S0735272718110018.

    Article  Google Scholar 

  22. A. M. Prodeus, V. S. Didkovskyi, “Objective estimation of the quality of radical noise suppression algorithms,” Radioelectron. Commun. Syst. 59, No. 11, 502 (2016). DOI: 10.3103/S0735272716110042.

    Article  Google Scholar 

  23. Y. Yang, “A signal theoretic approach for envelope analysis of real-valued signals,” IEEE Access 5, 5623 (2017). DOI: 10.1109/ACCESS.2017.2688467.

    Article  Google Scholar 

  24. “Time series forecasting using exponential smoothing,” (2011). URI: https://www.mql5.com/en/articles/318.

  25. J. Serra, L. Vincent, “An overview of morphological filtering,” Circuits Systems Signal Process. 11, No. 1, 47 (1992). DOI: 10.1007/BF01189221.

    Article  MathSciNet  MATH  Google Scholar 

  26. C. A. Aivazyan, L. D. Enyukov, and L. D. Meshalkin, Applied Statistics: Simulation Principles and Data Preprocessing [in Russian] (Finansy i Statistika, Moscow, 1983).

    Google Scholar 

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Authors and Affiliations

  1. Karpenko Physico-Mechanical Institute of NASU, Lviv, Ukraine

    A. B. Lozynskyy, I. M. Romanyshyn & B. P. Rusyn

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  1. A. B. Lozynskyy
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  2. I. M. Romanyshyn
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  3. B. P. Rusyn
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Correspondence to A. B. Lozynskyy, I. M. Romanyshyn or B. P. Rusyn.

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The authors declare that they have no conflict of interest.

Additional Information

The initial version of this paper in Russian is published in the journal “Izvestiya Vysshikh Uchebnykh Zavedenii. Radioelektronika,” ISSN 2307-6011 (Online), ISSN 0021-3470 (Print) on the link http://radio.kpi.ua/article/view/S0021347019050030 with DOI: https://doi.org/10.20535/S0021347019050030.

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Lozynskyy, A.B., Romanyshyn, I.M. & Rusyn, B.P. Intensity Estimation of Noise-Like Signal in Presence of Uncorrelated Pulse Interferences. Radioelectron.Commun.Syst. 62, 214–222 (2019). https://doi.org/10.3103/S0735272719050030

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  • DOI: https://doi.org/10.3103/S0735272719050030

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