Estimating Angular Coordinates of a Subarray Radar Using Artificial Neural Networks
https://doi.org/10.35596/1729-7648-2026-24-2-25-36
Abstract
This paper examines variants of a reflected signal angular coordinate meter using artificial neural networks for a frequency-agile radar based on an antenna array with subarrays, generating sum and difference channels, and an array of adapted receiving channels near the observation direction. A model of amplitude-phase- frequency non-identities of receiving channels during frequency agility and angular scanning is developed. Neural network architectures are proposed based on a multilayer perceptron for the case of creating sum-and-difference channels and a complex convolutional neural network and a multilayer perceptron for generating an array of adapted receiving channels. Modeling shows that the proposed architectures ensure the almost complete elimination of angular coordinate estimation errors associated with the presence of amplitude-phase-frequency non-identities of receiving channels, with an estimation accuracy close to the potentially achievable one.
About the Authors
I. Dubovik
Military Academy of Belarus
Belarus
Belarus
Cand. Sci. (Tech.), Associate Professor, Doctoral Student
Minsk
S. Kozlov
Belarusian State University of Informatics and Radioelectronics
Belarus
Belarus
Dr. Sci. (Tech.), Professor, Professor at the Department of Information Radiotechnologies
Minsk
I. Zaitsev
Belarusian State University of Informatics and Radioelectronics
Belarus
Belarus
Zaitsev Ilya, Postgraduate of the Department of Information Radiotechnologies
220013, Minsk, P. Brovki St., 6
Tel.: +375 17 293-89-11
P. Krivitsky
Belarusian State University of Informatics and Radioelectronics
Belarus
Belarus
Student
Minsk
References
1. Tatuzov A. L. (2009) Neural Networks in Radar Problems. Moscow, Radio Engineering Publ. (in Russian).
2. Bogoslovskaya M. A., Gavrilov K. Yu. (2007) Application of Neural Networks in Radar Target Direction Finding Problems. Phazotron. (3–4) (in Russian).
3. Morozova E. O., Ovchinnikov P. E., Semenova M. Yu. (2013) Neural Network Processing of Monopulse Radar. Vestnik of Lobachevsky University of Nizhni Novgorod. Series “Radiophysics”. (6), 62–66 (in Russian).
4. Kozlov S. V. (2018) Signal Processing of Little Elements Monopulse Direction Finder in the Conditions of Strong Interference Using Neural Networks. Doklady BGUIR. (5), 31–37 (in Russian).
5. Semenov L. M., Fridman L. B. (2023) Algorithm for Monopulse Measurement of the Angular Position of an Aircraft Using an Artificial Neural Network. Ural Radio Engineering Journal. (7), 291–303. DOI: 10.15826/urej.2023.7.3.004 (in Russian).
6. Shatsky N. V. (2022) Neural Network Method for Estimating the Angular Coordinates of Radar Targets in a Digital Antenna Array. T-Comm: Telecommunications and Transport. 16 (7), 4–13 (in Russian).
7. Ratynsky M. V., Porsev V. I. (ed.) (2019) Monopulse Direction Finding in Radars with Digital Phased Arrays. Moscow, Radio Engineering Publ. (in Russian).
8. Grigoriev L. N. (2010) Digital Beamforming in Phased Antenna Arrays. Moscow, Radio Engineering Publ. (in Russian).
9. Skolnik M. I. (2008) Radar Handbook. USA, McGraw-Hill Publ.
10. Wirth W. D. (2013) Radar Techniques Using Array Antennas. England, Stevenage, Institution of Engineering and Technology Publ.
11. Monzingo R. A., Miller T. W. (1986) Adaptive Antenna Arrays: Introduction to Theory. Moscow, Radio i Svyaz Publ. (in Russian).
12. Fenn A. J. (2008) Adaptive Antennas and Phased Arrays for Radar and Communications. USA, Artech House Publ.
13. Haykin S. (2014) Adaptive Filter Theory. UK, Pearson Publ.
14. Kozlov S. V., Le Van Cuong (2020) Selection of Implementation Parameters and Properties of Adaptive Maximum-Likelihood Algorithms for Estimating Target Angular Coordinates in a Radar Measuring Device with a Multi-Channel Receiving System. Science and Military Security. (1), 42–46 (in Russian).
15. Karpushin V. I., Kozlov S. V., Sergeev V. I. (2010) Synthesis of Variants of the Structure of Radar Angular Coordinate Meters with Adaptive Spatial Interference Compensation. Antennas. (6), 71–76 (in Russian).
Review
For citations:
Dubovik I., Kozlov S., Zaitsev I., Krivitsky P. Estimating Angular Coordinates of a Subarray Radar Using Artificial Neural Networks. Doklady BGUIR. 2026;24(2):25-36. (In Russ.) https://doi.org/10.35596/1729-7648-2026-24-2-25-36
Views:
148
JATS XML
Email this article 