Abstract
The applicability of the Kalman filter algorithm is explored with the aim of improving the accuracy of estimating the motion parameters of unmanned aerial vehicles (UAVs) is being considered. During flight, drones collect data from various sensors – such as GPS, inertial measurement units (IMUs), LiDAR, and ultrasonic sensors. However, these measurements are often affected by noise and errors. The Kalman filter is used to integrate and correct data from multiple sources, enabling accurate estimation of the UAV's current position, velocity, and spatial orientation. The study discusses the fundamental operating principles of this algorithm, including the prediction and update steps, and evaluates its effectiveness under conditions of noisy or partially available measurements. The results demonstrate that implementing the Kalman filter provides stable and accurate navigation of drones in dynamically changing environments. The findings may be useful in the design of autonomous navigation and control systems, as well as in remote sensing and digital map generation applications