A multi-sensor attitude information fusion based on RBF neural network algorithm

Because noise error and measurement error exist in the control of sensor data, using acceleration transducer and gyroscope separately cannot obtain the optimal posture angle. In order to solve this problem, a self-adaptive fusion estimation algorithm for multi-information measurement based on neural networks has been presented, which used the self-adaptive ability of neural networks to make real-time compensation and amendment for the state fusion estimation results. Compared with Kalman filtering method and Particle filtering method, this paper draws a conclusion that RBF neural network theory can obtain better fusion result, realize data fusion, and improve the detection precision of the attitude angle effectively.