APPLICATION OF CONVOLUTIONAL NEURAL NETWORKS FOR TECHNICAL OBJECT RECOGNITION IN THE INTERESTS OF RADIO MONITORING
Abstract
The article examines the possibility of using convolutional neural networks for technical object recognition in the context of radio monitoring. The focus is on the development and optimization of algorithms for processing radar signals using deep neural networks. Studies have shown that the use of CNN can significantly improve the classification accuracy of radio signals compared to traditional processingmethods. The developed approach is based on the extraction of hierarchical features from spectral images of radio signals and their subsequent classification using a trained neural network. The paper presents the results of experimental studies conducted on a dataset of more than 10,000 samples of radio signals of various types. It is shown that the proposed technique ensures recognition accuracy of up to 94% when working with noisy signals and the probability of a false alarm is no more than 0.05. Special attention is paid to the choice of neural network architecture for the specifics of the radio monitoring task. The options for converting radio signals into a spectral image for real-time processing were also considered in detail. Data preprocessing methods have been developed, including amplitude normalization, frequency correction, and interference elimination. The results of the study can be used in radio broadcast control systems and to ensure electromagnetic compatibility of electronic devices. The results obtained demonstrate the prospects of using CNN in the tasks of technical recognition of radio monitoring objects and open new opportunities for the development of intelligent radar information processing methods. Promising areas of further research include the development of adaptive neural network training methods in a changing radio environment and the creation of hybrid systems combining traditional signal processing methods with modern neural network algorithms
References
1. Радиоэлектронная борьба в Вооруженных силах США: военно-теоретический труд. – СПб.:
ООО Наукоемкие технологии, 2018. – 131 с.
2. РЛС военного назначения: Всевидящее Око в эпоху информационной войны // Серия углуб-
ленных отчетов по информатизации национальной обороны. – 2015. – № 2. – С. 25.
3. Грендер У. Лекции по теории образов. Т. 2. – М.: Мир, 1981. – C. 63-74.
4. Мазуров В.Д. Математические методы распознавания образов: учеб. пособие. – 2-е изд. – Ека-
теринбург: Урал, 2010. – C. 101-103.
5. Каргашин В.Л. Проблемы обнаружения и идентификации радиосигналов средств негласного
контроля // Специальная техника. – 2000. – № 3–5. – C. 45-48.
6. Ильин И.С. Материалы II Конференции «Стратегия развития технологий в области искусствен-
ного интеллекта для обеспечения национальной безопасности Российской Федерации» // Ис-
кусственный интеллект и направления его развития. Кубинка, 2018. – С. 29-33.
7. Ведяхин А.А., Бурцев М.С., Витяев Е.Е. Сильный искусственный интеллект: на подступах к
сверхразуму. – М.: 2021. – C. 205-215.
8. Нейронная сеть // Большая российская энциклопедия. Гл. ред. 5-е изд. – М.: Мир, 2016.
– С. 189-196.
9. Зорин А.В. Распознавание лиц на основе нейронных сетей. – 43-е изд. – СПб.: Синергия Наук,
2020. – C. 510-520.
10. Bengio Y., LeCun Y., Hinton G. Deep Learning // Nature. – 2015. – No. 521. – P. 436-444.
11. Bengio Y. Foundations and Trends in Machine Learning. – 2nd ed. Vol. 2. // Learning Deep Architectures
for AI / ed by Bengio Y. – Pekin: Resors, 2009. – P. 1-127.
12. Макаренко С.И. Справочник научных терминов и обозначений. – СПб.: Наукоемкие техноло-
гии, 2019. – 254 с.
13. Шумков Д.В. Классификация роя неконтролируемых дронов с использованием радиочастот на
основе методов машинного обучения в интересах РЭБ // Электронный рецензируемый сборник
научных статей по материалам секционного заседания. – Воронеж, 2023. – С. 153-159.
14. Богомолов Л.Ю. Контроль. Диагностика. – 4-е изд. Т. № 4. – СПб.: Мир, 2013. – C. 41-45.
15. Бутырский Е.Ю. Математическое моделирование: монография. – Lambert, Oslo, 2017.
16. Quinlan J.R. Induction of Decision Trees // Machine Learning. – 1986. – No. 1. – P. 81-106.
17. Schmidhuber J. Deep Learning in Neural Networks: An Overview. Vol. 61 // Neural Networks.
– Dalas, 2015. – P. 85-117.
18. Han J., Pei J., Kamber M. Data Mining: Concepts and Techniques. – New York: Elsevier, 2011.
– P. 740-742.
19. Song L. Neural Information Processing. – Kentuki: Bangma, 2013. – P. 36-44.
20. Haghighat M., Zonouz S., Abdel–Mottaleb M. CloudID: Trustworthy Cloud–based and Cross–
Enterprise Biometric Identification // Expert Systems with Applications. – 2015. – Vol. 42, No. 21.
21. Zadeh L. Fuzzy Logic, Neural Networks, and Soft Computing // Communications of the ACM.
– 2004. – P. 77-84.
22. Glauner P. Deep Convolutional Neural Networks for Smile Recognition (MSc Thesis). – Imperial
College London, Department of Computing, 2018. – URL: arXiv.com (дата обращения: 15.03.2023).
23. de Castro, Timmis J. Artificial Immune Systems: A New Computational Intelligence Approach.
– Springer, 2002. – P. 57-58.
