Е-Публикации
Технически университет - София

Abstract: This paper examines the integration of Kalman filter and Fuzzy algorithm in orientation measurement systems with MEMS sensors under dynamic conditions. The presented methodology aims to improve the accuracy and reliability of measurements by combining the advantages of both methods. The Kalman filter provides optimal state estimation by combining information from various sources and models, which reduces the influence of inertial disturbances and noise in the measurements. The fuzzy algorithm further improves the control and correction of measurements, especially at high speeds and rapid changes in the direction of movement. The proposed methodology for determining the variances in the Q and R matrices, based on the analysis of data from the MEMS gyroscope and the measurement system, demonstrates practical applicability and efficiency. This makes it suitable for a wide range of applications such as aviation, marine navigation, robotics, and autonomous transport systems, providing a basis for future research and optimization of algorithms for data processing and orientation control of moving objects.

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