Application of predictability index for process classification in information and communication systems

Pavlo Kostenko; Kostiantyn Vasiuta; Valeriy Slobodyanuk; Valerii Chystov; Mykyta Alonkin

doi:10.3103/S0735272724060013

Authors

Pavlo Kostenko Ivan Kozhedub Kharkiv National Air Force University, Kharkiv, Ukraine https://orcid.org/0000-0002-3382-0684
Kostiantyn Vasiuta Ivan Kozhedub Kharkiv National Air Force University, Kharkiv, Ukraine https://orcid.org/0000-0002-1978-3717
Valeriy Slobodyanuk Ivan Kozhedub Kharkiv National Air Force University, Kharkiv, Ukraine https://orcid.org/0000-0002-8291-8194
Valerii Chystov Ivan Kozhedub Kharkiv National Air Force University, Kharkiv, Ukraine https://orcid.org/0000-0002-4401-3773
Mykyta Alonkin Ivan Kozhedub Kharkiv National Air Force University, Kharkiv, Ukraine https://orcid.org/0009-0005-3019-8729

DOI:

https://doi.org/10.3103/S0735272724060013

Keywords:

information and communication systems, predictability index, identification scale, process classification, radioelectronic intelligence

Abstract

The modern development of information and communication systems (ICS) in the context of the widespread use of radio signals with statistical and dynamic characteristics close to those of white noise requires new approaches to the correct decision-making on the natural or artificial character of the process observed during the electronic intelligence.

In this paper, we propose to use the SG (Savit and Green) predictability index to classify the processes used in ICS. With the help of the proposed identification scale, which uses SG index as a parameter, the processes are analyzed and conventionally divided into three classes: IID-processes, chaotic processes with a destructured image in pseudo-phase space (IPPS), chaotic processes with a structured IPPS, and periodic processes. For each process, a histogram of the distribution of SG values on the identification scale is plotted.

Based on the conducted analysis, it was established that the identification scale based on the SG value effectively distinguishes not only classes of processes (for example, random, chaotic, and deterministic), but also processes within individual classes, for example, it distinguishes chaotic processes with destructured and structured IPPS. The results demonstrate statistical significance and reliability of the new classification method, making it promising for use in real ICS.

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