Recognition of Ballistic Missile Types Using Neural Networks Based on Trajectory Information
https://doi.org/10.35596/1729-7648-2025-23-5-58-65
Abstract
This article presents the results of solving the problem of recognizing ballistic missile types using neural networks based on trajectory information. Trajectory information from radar stations tracking these ballistic objects was used for recognition. Recognition using neural networks based on the “altitude – energy altitude” parameters is considered. Simulations showed that, with approximately equal solution times, the probability of correct recognition for feedforward neural networks (FFNN) is significantly higher than for the plane-cell-based algorithm used for comparison.
About the Authors
A. F. Apochkina
OJSC “AGAT – Control Systems” – Managing Company of “Geoinformation Control Systems” Holding (OJSC “AGAT”)
Belarus
Belarus
Alena F. Apochkina, Engineer-Programmer,
Minsk.
T. V. Prokofieva
OJSC “AGAT – Control Systems” – Managing Company of “Geoinformation Control Systems” Holding (OJSC “AGAT”)
Belarus
Belarus
Tatiana Vladimirovna Prokofieva, Leading Engineer-Programmer,
220114, Minsk, Nezavisimosti Ave., 117.
Tel.: +375 29 502-64-37.
U. A. Aparovich
OJSC “AGAT – Control Systems” – Managing Company of “Geoinformation Control Systems” Holding (OJSC “AGAT”)
Belarus
Belarus
Uladzimir A. Aparovich, Cand. Sci. (Tech.), Leading Systems Analyst,
Minsk.
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Review
For citations:
Apochkina A.F., Prokofieva T.V., Aparovich U.A. Recognition of Ballistic Missile Types Using Neural Networks Based on Trajectory Information. Doklady BGUIR. 2025;23(5):58-65. (In Russ.) https://doi.org/10.35596/1729-7648-2025-23-5-58-65
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