Journal of Radio Electronics. eISSN 1684-1719. 2026. №2
Full text in Russian (pdf)
DOI: https://doi.org/10.30898/1684-1719.2026.2.10
Detection of pulse signals
in a quasi-vertical ionospheric channel
based on a statistical model of a mixture
of Rayleigh and rice distributions
Y.V. Davydov, I.V. Skvortsov, R.R. Latypov, D.V. Davydov
Kazan Federal University
420111, Russia, Kazan, Kremlevskaya str., 16a
The paper was received December 18, 2025.
Abstract. In this paper we propose and test a method for detecting pulsed signals in a quasi-vertical ionospheric shortwave channel based on a statistical model of a mixture of Rayleigh and Rice distributions. Traditional threshold detection methods prove insufficiently reliable in conditions of high noise levels and channel instability. The proposed method considers the received signal as a mixture of two components: noise (described by the Rayleigh distribution) and signal (described by the Rice distribution). Experimental results confirm the feasibility of the proposed method for detecting pulsed signals under low signal-to-noise conditions and allow to estimate the signal propagation probability depending on the time of day. The proposed method improves the stability of NVIS communications in the presence of strong interference and dynamically changing ionospheric conditions.
Key words: ionospheric short-wave channel, NVIS, mixture model, Rice distribution, Rayleigh distribution, iterative algorithm.
Financing: The work is carried out in accordance with the Strategic Academic Leadership Program «Priority 2030» of the Kazan Federal University of the Government of the Russian Federation.
Corresponding author: Davydov Yuriy Vladimirovich, davydovkfu@mail.ru
References
1. Witvliet, B. A., and R. M. Alsina-Pagès. Radio communication via Near Vertical Incidence Skywave propagation: an overview // Telecommunication systems. – 2017. – T.66. – №.2. – C.295-309.
2. Egoshin I.A., Kolchev A.A., Nedopekin A.E. Obnaruzhenie signala ionozonda s lineinoi chastotnoi modulyatsiei v usloviyakh apriornoi neparametricheskoi neopredelennosti // Izvestiya vuzov. Radiofizika. – 2019. – T.62. – №.10. – S.769–778.
3. Recommendation ITU-R P.1057-7. Probability Distributions Relevant to Radiowave Propagation Modelling. Geneva: ITU, 2022.
4. Allen, Jeffery, et al. Mid-latitude mobile wideband HF-NVIS channel analysis: Part 1. – 2017. – №. SPAWARTR3075.
5. Hastie, T. et al. The Elements of Statistical Learning. New York, NY, USA: Springer New York Inc., 2001.
6. D. Barber, Bayesian Reasoning and Machine Learning. Cambridge: Cambridge University Press, 2012.
7. MacGougan G. D. Poor receiving conditions effect on the GPS positioning accuracy//In Proceedings of ION GNSS 2012. – Neshvil, 2012, C.204-216.
8. Voroshilin E. P., Mironov M. V., Gromov V. A. Opredelenie vremeni zaderzhki priema signalov gruppirovkoi prostranstvenno-raznesennykh malykh kosmicheskikh apparatov //Mater. dokl. Vseros. radiofizicheskikh nauchnykh chtenii-konferentsii pamyati NA Armanda. – 2010. – C.75.
For citation:
Davydov Y.V., Skvortsov I.V., Latypov R.R., Davydov D.V. Detection of pulse signals in a quasi-vertical ionospheric channel based on a statistical model of a mixture of Rayleigh and Rice distributions. // Journal of Radio Electronic. – 2026. – №. 2. https://doi.org/10.30898/1684-1719.2026.2.10 (in Russian)