Software Implementation of UAV Navigation Algorithms Using Telemetry Processing Systems
Students Name: Romaniv Viktor Pavlovych
Qualification Level: magister
Speciality: System Administration of Telecommunications Networks
Institute: Institute of Information and Communication Technologies and Electronic Engineering
Mode of Study: full
Academic Year: 2025-2026 н.р.
Language of Defence: ukrainian
Abstract: Unmanned Aerial Vehicles (UAVs) are increasingly applied in civilian, scientific, and industrial domains, including environmental monitoring, logistics, precision agriculture, and defense. The reliability of UAV operation largely depends on the efficiency of its navigation algorithms and the accuracy of telemetry data used to determine position, orientation, and flight parameters. Therefore, the development of software systems capable of collecting, filtering, and integrating telemetry from various sensors has become a key direction in modern UAV research [1]. This master’s thesis focuses on the software implementation of UAV navigation algorithms based on telemetry processing systems, combining data from GPS, IMU, barometric sensors, and optical cameras. According to Chen et al. [2], the integration of multiple data sources through sensor fusion significantly improves UAV navigation accuracy in complex conditions such as GPS signal loss, noise interference, and sensor drift. Special attention is paid to methods of noise filtering, sensor fusion, and position estimation using algorithms such as the Extended Kalman Filter (EKF), Complementary Filter, and machine learning–based correction models [4]. The research methodology includes both theoretical and experimental analysis of the UAV navigation pipeline — from telemetry acquisition to decision-making in autonomous flight control. The software implementation was carried out in the Python programming environment, using libraries such as NumPy, OpenCV, and Matplotlib. Following the ideas of Sharma [4], a modular architecture was designed to support real-time data fusion and adaptive navigation. Three main experimental studies were conducted: 1. filtering of telemetry data using EKF versus IMU-only measurements; 2. GPS + IMU + barometer sensor fusion testing; 3. Map-matching and trajectory correction under simulated GPS jamming conditions [5]. These experiments enabled comparison of navigation accuracy, stability, and recovery time after telemetry degradation. The results demonstrated that integrated telemetry processing enhances positioning precision by up to 25% compared to single- sensor navigation, [2] confirming findings from earlier works [4]. The research objectives are as follows: 1. To analyze the role and structure of telemetry in UAV navigation systems. 2. To study existing navigation algorithms and methods of sensor data integration. 3. To design and implement software modules for telemetry acquisition and filtering. 4. To perform experimental evaluation of navigation performance under simulated interference conditions. 5. To assess the economic feasibility of developing such a telemetry-based navigation subsystem. The object of research is the UAV navigation process based on telemetry data. The subject of research is the software algorithms and data-processing methods ensuring autonomous navigation under uncertain conditions. Research methods include theoretical analysis, simulation modeling, and experimental testing in Python. The first chapter presents an overview of current methods of UAV navigation and telemetry acquisition, including GPS, IMU, barometric, and vision-based systems. The second chapter outlines the theoretical foundations of navigation algorithms, focusing on motion models, trajectory planning, stabilization, and visual object recognition. The third chapter presents the software implementation of navigation algorithms in Python, describing system architecture, data processing modules, and visualization tools. The fourth chapter contains experimental studies, including telemetry filtering, multi-sensor data fusion, and map-matching for trajectory correction. The fifth chapter provides a techno-economic justification of the project, evaluating costs, expected benefits, and payback period for the developed subsystem. The conducted research demonstrated that software-based telemetry processing significantly enhances UAV navigation accuracy and robustness, allowing stable flight even during partial loss of satellite positioning data. The implemented algorithms can be applied both for academic purposes and as a prototype for real UAV navigation systems [3].