Удосконалення методу оцінки динамічного повітряного середовища при інформаційному забезпеченні диспетчера управління повітряним рухом

Abstract

This research provides a formal scientific formulation for the modernization of Dynamic Air Environment (DAE) estimation methods, specifically designed to enhance the decision-support infrastructure for Air Traffic Controllers (ATC). The study addresses the critical challenge of managing high information complexity arising from the integration of heterogeneous data sources, such as weather radars, ADS-B telemetry, wind profilers, and GNSSderived atmospheric estimations. The author identifies that the stochastic instability of local meteorological phenomena – including wind shear and turbulence – equires a transition from traditional estimation techniques to advanced data fusion algorithms capable of real-time adaptation. A significant portion of the paper is dedicated to the analytical identification of "anomalous" measurements. The research evaluates the efficacy of statistical criteria based on residual innovations within Kalman-type filters, utilizing the Mahalanobis distance logic: γ𝑘= ν𝑘𝑇𝑆𝑘−1ν𝑘 This approach allows the system to objectively detect and isolate signal interruptions or turbulent noise, preventing corrupted data from entering the atmospheric state model. Furthermore, the study investigates the transition to predictive operating modes during sensor degradation, comparing the performance of classical stochastic models (ARIMA) with advanced deep learning architectures, such as Long Short-Term Memory (LSTM) and Gated Recurrent Units (GRU).The research further emphasizes the necessity of adaptive weighting mechanisms, which dynamically prioritize data streams based on their real-time signal-to-noise ratios and reliability metrics. This is increasingly critical as modern airspaces must accommodate the complex integration of Unmanned Aircraft Systems (UAS) and Urban Air Mobility (UAM), where localized micro-atmospheric stability is a primary safety determinant. The author establishes a dual-mode efficiency framework – Correction and Prediction–aimed at ensuring that at least 80% of trajectory-affecting disturbances are compensated for by the information system. By aligning these integrated multi -sensor methodologies with ICAO standards, the study provides a robust foundation for maintaining optimal situational awareness. The article concludes that these algorithmic improvements are essential for reducing the cognitive load on ATC personnel, enhancing runway throughput, and ensuring absolute safety in a technologically saturated aviation environment.