Implementation and Comparison of Attitude Estimation Algorithms Using Low-Cost Sensors

In a flying system, attitude control is one of the essential subsystems. In this subsystem, estimating the current state is very important to control the state, which is achieved by considering the attitude sensors. Comprehensive research is being done today to reduce the cost of Attitude sensors in applications such as drones, satellite simulation platforms, etc. For this purpose, sensors based on Micro-electromechanical Systems have received much attention due to their small size and low energy consumption. This model of sensors, despite its many advantages, has various noises and disturbances that require the application of fusion and estimation algorithms to obtain an acceptable output. In this research, to determine the attitude of the test platform, data fusion algorithms including complementary filter, Kalman filter, and Extended Kalman filter are implemented on a low-cost sensor. The mentioned estimation methods were implemented on the test platform and by determining the effective parameters in the estimation algorithms, the desired accuracy was obtained. The module obtained in these experiments is comparable to more expensive sensors.