Ensuring the resilient navigation of unmanned aerial vehicles (UAVs) under conditions of limited or unstable sensor information is one of the key challenges of modern autonomous mobility within smart infrastructure and sustainable development. This article proposes an intelligent autonomous UAV control method based on the integration of geometric trajectory modeling, neural network-based sensor data filtering, and reinforcement learning. The geometric model, constructed using path coordinates, allows the trajectory tracking problem to be formalized as an affine control system, which ensures motion stability even in cases of partial data loss. To process noisy or fragmented GPS and IMU signals, an LSTM-based recurrent neural network filter is implemented. This significantly reduces positioning errors and maintains trajectory stability under environmental disturbances. In addition, the navigation system includes a reinforcement learning module that performs real-time obstacle prediction, path correction, and speed adaptation. The method has been tested in a simulated environment with limited sensor availability, variable velocity profiles, and dynamic obstacles. The results confirm the functionality and effectiveness of the proposed navigation system under sensor-deficient conditions. The approach is applicable to environmental monitoring, autonomous delivery, precision agriculture, and emergency response missions within smart regions. Its implementation contributes to achieving the Sustainable Development Goals (SDG 9, SDG 11, and SDG 13) by enhancing autonomy, energy efficiency, and the safety of flight operations.

The article presents a mathematical model of radar systems operating under jamming conditions. The relevance of the study is highlighted by two main factors: the need to enhance the accuracy of radar systems and the requirement to incorporate new jamming factors into models, which raises challenges in maintaining the reliability of mathematical formalizations. Various modeling approaches, including Markov models, deep learning, and probabilistic filters, are analyzed. These methods enable radar systems to adapt to changing conditions and improve target detection, which are crucial for their effective operation under uncertainty. The article emphasizes the importance of integrating different techniques to develop algorithms capable of functioning in complex jamming environments. A key aspect of the model is its consideration of time delays and probabilistic characteristics, which help to formalize and optimize the system’s performance in various jamming scenarios. The model is based on the Graphical Evaluation and Review Technique (GERT) network, offering in-depth analysis and adaptation of radar systems to changing conditions. This approach is a critical component for enhancing the resilience of radar systems in complex and uncertain operational environments.