DOI
10.34229/KCA2522-9664.25.5.16
UDC 621.391
P. Kostenko
Ivan Kozhedub Kharkiv National Air Force University, Kharkiv, Ukraine,
kpyu@ukr.net
K. Vasiuta
Ivan Kozhedub Kharkiv National Air Force University, Kharkiv, Ukraine,
kohafish@ukr.net
V. Slobodyanuk
Ivan Kozhedub Kharkiv National Air Force University, Kharkiv, Ukraine,
sloval@i.ua
R. Kachailo
Ivan Kozhedub Kharkiv National Air Force University, Kharkiv, Ukraine,
rkacajlo@gmail.com
EVALUATION OF THE QUALITY OF NOISE REDUCTION IN SIGNALS
WITH AMPLITUDE AND PHASE MANIPULATION USING
THE ATS ALGORITHM
Abstract. The article studies the quality of signal recovery distorted by additive noise using the ATS algorithm (Attractor Trajectory Surrogates), which reduces the influence of additive noise in observations of signals with ASK and PSK by manipulating their parameters. To determine the numerical measure of signal recovery quality, the use of SG statistics is proposed, which allows obtaining the corresponding numerical values of the index of predictability of the results of signal observation and recovery. The methodological error of signal recovery that occurs when using the ATS algorithm is also considered. It is shown that the use of SG statistics as a measure of signal recovery error allows determining its efficiency. Numerical values of signal recovery errors are obtained, and the methodological errors of the ATS algorithm for ASK and PSK signals manipulated by “white” and “color” messages are analyzed. It is found that the algorithm parameters affect the signal recovery error. Recommendations are given for the parameters of the ATS algorithm, which ensure its better efficiency in terms of sensitivity to noise levels and their distributions. It is shown that the contribution of methodological error to the signal recovery error decreases relative to the noise level as it increases. The proposed ATS algorithm is characterized by larger average values of SG statistics compared to the case where this algorithm is not used.
Keywords: SG statistics, predictability index, ATS algorithm, recovery errors, ASK and PSK signals.
full text
REFERENCES
- 1. Theiler, J., Eubank S., Longtin A., Galdrikian B., Farmer D. Testing for nonlinearity in time series: the method of surrogate data. Physica Nonlinear Phenomena. 1992. Vol. 58, N 1–4. Р. 77–94. https://doi.org/10.1016/0167-2789(92)90102-S.
- 2. Kantz H., Schreiber T. Nonlinear time series analysis. Cambridge University Press, 2004. 388 p.
- 3. Small M. Attractor trajectory surrogates: hypothesis testing and prediction. International Symposium on Nonlinear Theory and its Applications. Fukuoka, Japan, 2004. P. 123–126.
- 4. Guarin D., Orozco А., Delgado Е. A new surrogate data method for nonstationary time series. 2010. URL: https://www.researchgate.net/publication/45934143_.
- 5. Kostenko P.Yu., Vasyuta K.S., Slobodanyuk V.V., Yakovenko D.S. Using surrogate signals to improve the quality of parameter estimation of regular and chaotic signals observed against the background of additive noise. Control, navigation and communication systems. 2010. No. 4(16). P. 28–32.
- 6. Kostenko P., Vasylyshyn V. Surrogate data generation technology using the SSA method for enhancing the effectiveness of signal spectral analysis. Radioelectronics and Communications Systems. 2015. N 58. Р. 356–361. https://doi.org/10.3103/S0735272715080038.
- 7. Kostenko P., Slobodyanuk V., Kostenko L. Method of image denoising in generalized phase space with improved indicator of spatial resolution. Radioelectronics and Communications Systems. 2019. N 62. Р. 368–375. https://doi.org/10.3103/S0735272719070045.
- 8. Vasyuta K.S. Formation of response imitation interference to radio engineering systems using surrogate signals. Information processing systems. 2013. No. 4 (111). P. 12–15.
- 9. Vasyuta K.S., Slobodyanyuk V.V., Tantsyura O.B., Kachaylo R.O. Comparative analysis of methods for forming active interference of the range tracking channel for radar stations using surrogate data technology. Weapons Systems and Military Equipment. 2022. No. 4. P. 73–80. https://doi.org/10.30748/soivt.2022.72.08.
- 10. Savit R., Green M. Time series and dependent variables. Physica D: Nonlinear Phenomena. 1991. Vol. 50, N 1. P. 95–116. https://doi.org/10.1016/0167-2789(91)90083-L.
- 11. Kostenko P.Yu., Slobodyanyuk V.V., Alyonkin M.I., Shapovalov O.V. Application of the predictability index to estimate the signal delay time under conditions of multiplicative interference with unknown probability distribution density. Collection of scientific papers of the Kharkiv National University of the Air Force. 2024. No. 1 (79). P. 52–59. https://doi.org/10.30748/zhups.2024.79.08.
- 12. Dai H., Sun F., Jiang W., Xiao K., Zhu X., Liu J.. Application of wavelet decomposition and singular spectrum analysis to GNSS station coordinate time series. Geomatics and Information Science of Wuhan University. 2021. Vol. 46, N 3. P. 371–380. https://doi.org/10.13203/j.whugis20190107.
