Методи обробки сигналів для ідентифікації та класифікації повітряних об'єктів на основі аналізу шумових характеристик

Abstract

This study explores signal-processing methods for identifying and classifying airborne objects in noisy environments. The research focuses on the application of Kalman filters to minimize prediction errors and the use of Fourier transform-based spectral analysis to detect characteristic frequencies of various objects. Classification approaches leveraging noise characteristics and machine learning techniques are proposed to improve recognition accuracy. Additionally, the study emphasizes the importance of adapting signal processing algorithms to dynamic interference conditions to enhance classification reliability.