Integration and coordination of electronic warfare assets through large-scale language models

Authors

DOI:

https://doi.org/10.15587/2706-5448.2025.323916

Keywords:

electronic warfare, large language models, artificial intelligence, multi-agent structures, knowledge base, executive modules

Abstract

As an object of research, the work considers the process of functioning of electronic warfare (EW) means using artificial intelligence (AI) technologies based on large language models (LLM). One of the most problematic issues in increasing the efficiency of their functioning is ensuring the adaptability function in EW means, as well as timely detection of threats and formation of appropriate countermeasures. This problem is solved by implementing a multi-agent architecture, the task of which is to ensure continuous exchange of information, both between agents in the EW means themselves and in the system as a whole.

The considered method of increasing the adaptability of the system due to LLM with self-learning mechanisms provides the system with the opportunity to improve its data processing algorithms, promptly detect new types of signals and respond to changes in the parameters of the enemy's REM. Using the Retrieval-Augmented Generation (RAG) approach allows to detect and enter new types of signals into the database and quickly form appropriate recommendations for countermeasures.

An equally important component is the use of combining several EW tools into a single information network. This approach will ensure the consistency of the actions of all EW tools (agents) and the rapid exchange of information between them.

Taking into account the above, there is a possibility of significantly increasing the adaptability and efficiency of EW systems by integrating multi-agent structures using LLM, which allow optimizing resource allocation and making decisions in real time. This will ensure a high level of adaptation of EW tools, which is an important feature for working in conditions of dynamically changing electromagnetic environments.

Thanks to the proposed architecture and the use of appropriate algorithms, it is possible to obtain high indicators of classification accuracy and signal processing speed, which positively affects the adaptability of the system and the overall effectiveness of countering threats.

Author Biographies

Vadym Kozlov, Central Scientific Research Institute of Armament and Military Equipment of Armed Forces of Ukraine

PhD

Scientific and Organizational Department

Vadym Slyusar, Central Scientific Research Institute of Armament and Military Equipment of Armed Forces of Ukraine

Doctor of Technical Sciences, Professor, IEEE Member, Chief Researcher, Group Leader

Volodymyr Tverdokhlibov, Central Scientific Research Institute of Armament and Military Equipment of Armed Forces of Ukraine

PhD, Senior Researcher, Head of Research Department

Zoia Andriichuk, Central Scientific Research Institute of Armament and Military Equipment of Armed Forces of Ukraine

Researcher

Scientific and Information Department

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Integration and coordination of electronic warfare assets through large-scale language models

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Published

2025-02-27

How to Cite

Kozlov, V., Slyusar, V., Tverdokhlibov, V., & Andriichuk, Z. (2025). Integration and coordination of electronic warfare assets through large-scale language models. Technology Audit and Production Reserves, 1(2(81), 54–61. https://doi.org/10.15587/2706-5448.2025.323916

Issue

Vol. 1 No. 2(81) (2025): Information and control systems

Section

Systems and Control Processes

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Copyright (c) 2025 Vadym Kozlov, Vadym Slyusar, Volodymyr Tverdokhlibov, Zoia Andriichuk

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