Кафедра аеронавігації, метеорології та організації повітряного руху ФАМ (створена з 1 липня 2022 року) https://dspace.usfa.edu.ua/handle/123456789/1903 2026-10-16T07:08:47Z 2026-10-16T07:08:47Z Пальоний, Андрій Сергійович Пасічна, Вікторія Вікторівна Palionyi, Andrii Pasichna, Viktoriia https://dspace.usfa.edu.ua/handle/123456789/2944 2026-09-30T07:04:00Z 2026-04-08T00:00:00Z

Title: Проблемні питання розробки систем динамічної генерації сценаріїв для сучасних диспетчерських тренажерів Authors: Пальоний, Андрій Сергійович; Пасічна, Вікторія Вікторівна; Palionyi, Andrii; Pasichna, Viktoriia Abstract: This study addresses automating scenario creation for air traffic control simulators within the CBTA framework. Manual modeling limits training variability and causes a "learned scenario" effect; therefore, a data-driven approach is proposed to automatically adapt task complexity to the cadet's level. The paper analyzes technical barriers, including data incompleteness and the complexity of modeling dynamic ATC behavior. The findings offer mechanisms to enhance training realism while optimizing the workload for instructors.

2026-04-08T00:00:00Z Калашник, Ганна Анатоліївна Одайник, Борис Володимирович Kalashnyk, Hanna Odainyk, Borys https://dspace.usfa.edu.ua/handle/123456789/2943 2026-09-30T06:58:25Z 2026-04-08T00:00:00Z

Title: Постановка наукового завдання удосконалення методів прогнозування погодних умов для покращення управління польотом БПЛА Authors: Калашник, Ганна Анатоліївна; Одайник, Борис Володимирович; Kalashnyk, Hanna; Odainyk, Borys Abstract: This research provides a comprehensive and formalized statement of the scientific task aimed at bridging the critical operational gap between macro-scale atmospheric modeling and the high-precision requirements of autonomous Unmanned Aerial Vehicle (UAV) flight control loops. The author identifies that contemporary Numerical Weather Prediction (NWP) models, while effective for general aviation, lack the spatial and temporal resolution necessary to detect micro-scale phenomena – such as urban wind tunnels, localized turbulence, and rapid icing – which directly jeopardize the stability of light and medium-range UAVs. This discrepancy creates a significant scientific challenge: the need for a localized, real-time predictive framework capable of accounting for atmospheric stochasticity. The study proposes a paradigm shift toward an intelligent predictive ecosystem integrated directly into the Flight Control System (FCS). The core of this scientific task involves the development of a predictive functional 𝑓(𝑀𝑡, 𝑆𝑡, 𝑃𝑡) designed to minimize the prediction error ϵ by leveraging advanced Deep Learning architectures, specifically Recurrent Neural Networks (RNN) and Long Short-Term Memory (LSTM) models. These AI-driven tools are tasked with the continuous assimilation of heterogeneous data streams from onboard sensors, ground-based meteorological stations, and satellite feeds.Furthermore, the paper explores the mathematical integration of these high-fidelity forecasts into the autonomous control law 𝑢 = ϕ(𝑍𝑡, 𝑀𝑡+1 ̂). This integration enables the transition from legacy reactive compensation to proactive trajectory management, allowing the autopilot to anticipate and neutralize atmospheric disturbances before they affect flight integrity. The research substantiates that "weather-aware" autonomy is essential for optimizing battery energy consumption, enhancing mission reliability in austere environments, and ensuring the safe, systematic integration of autonomous systems into the U-space framework. Ultimately, the article concludes that the proposed multidisciplinary approach, combining aviation meteorology with reinforcement learning, is fundamental for achieving the airworthiness standards required for future digital and highly congested airspaces regulated by ICAO.

2026-04-08T00:00:00Z Калашник, Ганна Анатоліївна Нестеренко Олександр Миколайович, Нестеренко Олександр Миколайович Kalashnyk, Hanna Nesterenko, Oleksandr https://dspace.usfa.edu.ua/handle/123456789/2942 2026-09-23T08:53:45Z 2026-04-08T00:00:00Z

Title: Постановка наукового завдання удосконалення систем навігації та управління безпілотних літальних апаратів у дестабілізуючих умовах Authors: Калашник, Ганна Анатоліївна; Нестеренко Олександр Миколайович, Нестеренко Олександр Миколайович; Kalashnyk, Hanna; Nesterenko, Oleksandr Abstract: The paper analyzes methods for autonomous UAV navigation and control in destabilizing conditions, such as GNSS-denied environments and electronic countermeasures. The impact of non-Gaussian noise and outliers on inertial system accuracy is examined. The study proposes integrating Visual SLAM with Euclidean Signed Distance Fields (ESDF) for safe trajectory planning. Reliability is enhanced through robust filtering (H∞ -MCUKF and AMEECKF) to suppress interference and stabilize state estimation. The prospects of using AI, including reinforcement learning and recurrent neural networks, for adaptive control and obstacle avoidance under sensor data deficiency are substantiated.

2026-04-08T00:00:00Z Калашник-Рибалко, Мирослава Анатоліївна Мордован, Андрій Віталійович https://dspace.usfa.edu.ua/handle/123456789/2941 2026-09-23T08:42:46Z 2026-04-08T00:00:00Z

Title: Удосконалення методу оцінки динамічного повітряного середовища при інформаційному забезпеченні диспетчера управління повітряним рухом Authors: Калашник-Рибалко, Мирослава Анатоліївна; Мордован, Андрій Віталійович 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.

2026-04-08T00:00:00Z