The article considers the problem of detecting a useful harmonic signal in noise for a small signal-to-noise ratio. A new detection mechanism based on the construction of a stochastic matrix based on a single signal window is proposed. The properties of the considered limit matrices and their limit vectors are investigated and established, for which the corresponding lemmas and theorems are formulated and proved. Formulas are obtained that relate these vectors to the noise envelope with known parameters.
Keywords: signal processing, multiple improper integrals, Fourier transform, signal detection in noise, random stochastic matrix
Received October 8, 2025
Revised December 18, 2025
Accepted December 22, 2025
Andrey Alexeevich Galyaev, Dr. Eng. Sci., Institute of Control Sciences of the Russian Academy of Sciences, Moscow, 117997 Russia, e-mail: galaev@ipu.ru
Leonid Mikhailovich Berlin, Institute of Control Sciences of the Russian Academy of Sciences, Moscow, 117997 Russia, e-mail: berlin.lm@phystech.edu
Pavel Vladimirovich Lysenko, Cand. Sci (Phys.-Math.), Institute of Control Sciences of the Russian Academy of Sciences, Moscow, 117997 Russia, e-mail: pavellysen@ipu.ru
Vladimir Georgievich Babikov, Cand. Sci (Phys.-Math.), Institute of Control Sciences of the Russian Academy of Sciences, Moscow, 117997 Russia, e-mail: babikov@ipu.ru
Anton Pavlovich Potapov, Institute of Control Sciences of the Russian Academy of Sciences, Moscow, 117997 Russia, e-mail: potapov@ipu.ru
REFERENCES
1. Dobrushin R., Pinsker M., Shiryaev A. Application of the notion of entropy in the problems of detecting a signal in noise. Lith. Math. J., 1963, vol. 3, no. 1, pp. 107–118. https://doi.org/10.15388/LMJ.1963.19410
2. Shiryaev A.N. Veroyatnostno-statisticheskie metody v teorii prinyatiya reshenii (Probabilistic-statistical methods in decision-making theory). Moscow: MTSNMO: NMU, 2020. 3rd ed.
3. Galyaev A.A., Berlin L.M., Lysenko P.V., Babikov V.G. Order statistics of the normalized spectral distribution for detecting weak signals in white noise. Autom. Remote Control., 2024, vol. 85, no. 12, pp. 1041–1055. https://doi.org/10.1134/S0005117924700401
4. Kishan G.M., Chilukuri K.M., HuaMing H. Anomaly detection principles and algorithms. Cham: Springer, 2017. https://doi.org/10.1007/978-3-319-67526-8
5. Johnson P., Moriarty J., Peskir G. Detecting changes in real-time data: a user’s guide to optimal detection. Philos. Trans. Royal Soc. A. 2017. Vol. 375 (2100): 20160298. https://doi.org/10.1098/rsta.2016.0298
6. Cohen L. Time-frequency analysis. N.J.: Prentice-Hall, 1995.
7. Rihaczek A. Signal energy distribution in time and frequency. IEEE Trans. Inf. Theory, 1968, vol. 14, no. 3, pp. 369–374. https://doi.org/10.1109/TIT.1968.1054157
8. Ronald L.A., Duncan W.M. Signal analysis: time, frequency, scale, and structure. N.J.: IEEE Press, 2004.
9. Lysenko P., Galyaev A., Berlin L., Babikov V. Information complexity of time-frequency distributions of signals in detection and classification problems. Entropy, 2025, vol. 27, no. 10. https://doi.org/10.3390/e27100998
10. Lehner F., Mingo J.A., Speicher R. Free probability and random matrices. Jahresber. Dtsch. Math. Ver., 2019, vol. 121, pp. 147–151. https://doi.org/10.1365/s13291-018-0191-z
11. Bishop A.N., Moral P.D., Angéle Niclas A. An introduction to Wishart matrix moments. Found. Trends Mach. Le., 2018, vol. 11, no. 2, pp. 97–218. https://doi.org/10.1561/2200000072
12. Zunino L., Soriano M.C., Rosso O.A. Distinguishing chaotic and stochastic dynamics from time series by using a multiscale symbolic approach. Phys. Rev. E. Stat. Nonlin. Soft. Matter Phys., 2012, vol. 86, no. 4, pp. 1–5. https://doi.org/10.1103/PhysRevE.86.046210
13. Couillet R., Debbah M. Random matrix methods for wireless communications. NY: Cambridge University Press, 2011. https://doi.org/10.1017/CBO9780511994746
14. Klimov S.A. Upgrading of the computational efficiency of the Rayleigh super resolution methods. J. Radio Electronics, 2012, no. 3, p. 1–11, ISSN: 1684-1719 (in Russian).
15. Galyaev A.A., Babikov V.G., Lysenko P.V., Berlin L.M. A new spectral measure of complexity and its capabilities for detecting signals in noise. Dokl. Math., 2024, vol. 110, no. 1, pp. 361–368. https://doi.org/10.1134/S1064562424702235
16. Berlin L.M., Galyaev A.A., Lysenko P.V. Comparison of information criteria for detection of useful signals in noisy environments. Sensors, 2023, vol. 23, no. 4, art. no. 2133. https://doi.org/10.3390/s23042133
17. Babikov V.G., Galyaev A.A. Analytical representation of complexity diagrams. Probl. Inf. Transm., 2025, vol. 61, no. 1, pp. 27–40. https://doi.org/10.1134/S003294602501003X
Cite this article as: A.A. Galyaev, L.M. Berlin, P.V. Lysenko, V.G. Babikov, A.P. Potapov. Properties of random stochastic matrices in the detection problem for a small signal-to-noise ratio. Trudy Instituta Matematiki i Mekhaniki UrO RAN, 2026, vol. 32, no. 1, pp. 88–104.