<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>This article presents a customized approach for training a supervised learning neural network with the adaptive moment estimation algorithm, to classify the number of frequency hopping networks in an operational area. The algorithm was constructed based on data experimentally collected from a real-time spectrum analyzer for military very high frequency hopping networks. The impact of some training parameters on classification efficiency is briefly discussed while the obtained accuracy was above 97% for both test and validation data in all training variations. With this promising result, the proposed algorithm has the potential to be utilized in developing operational systems capable of real-time signal reconnaissance for military frequency hopping radio networks.</p>
</abstract>

This article presents a customized approach for training a supervised learning neural network with the adaptive moment estimation algorithm, to classify the number of frequency hopping networks in an operational area. The algorithm was constructed based on data experimentally collected from a real-time spectrum analyzer for military very high frequency hopping networks. The impact of some training parameters on classification efficiency is briefly discussed while the obtained accuracy was above 97% for both test and validation data in all training variations. With this promising result, the proposed algorithm has the potential to be utilized in developing operational systems capable of real-time signal reconnaissance for military frequency hopping radio networks.

Machine-Learning-Based Classification of Frequency Hopping in Radio Networks for Communication Reconnaissance

International conference KNOWLEDGE-BASED ORGANIZATION

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

This article presents a customized approach for training a supervised learning neural network with the adaptive moment estimation algorithm, to classify the...

Machine-Learning-Based Classification of Frequency Hopping in Radio Networks for Communication Reconnaissance

Online veröffentlicht: 19. Juli 2023

Seitenbereich: 78 - 84

DOI: https://doi.org/10.2478/kbo-2023-0079

Schlüsselwörter
machine learning, frequency hopping, communication reconnaissance

© 2023 Annamaria Sârbu et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Machine-Learning-Based Classification of Frequency Hopping in Radio Networks for Communication Reconnaissance

Online veröffentlicht: 19. Juli 2023

Seitenbereich: 78 - 84

DOI: https://doi.org/10.2478/kbo-2023-0079

Schlüsselwörtermachine learning, frequency hopping, communication reconnaissance

© 2023 Annamaria Sârbu et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Schlüsselwörter
machine learning, frequency hopping, communication reconnaissance