Multi-sensor Data Fusion Using Adaptive Kalman Filter

Abstract

Accurate attitude information is essential and crucial for autonomous underwater vehicle (AUV) to achieve the purpose of precise control. However, there is an error between the measured value and the real value due to the influence of noise on sensor data acquisition. To obtain high-precision attitude information, this paper presents a data fusion method using adaptive Kalman filter to fuse data of multi-sensor which is integrated gyroscope, accelerometer and magnetometer. An adaptive fuzzy logic system (AFLS) is utilized to improve the fusion accuracy in the state estimation. The stability, static accuracy and dynamic tracking of the adaptive Kalman filtering algorithm are tested and analyzed through experiments. The experimental results show that the improved covariance adaptive Kalman filtering algorithm can fuse the measured values of the three sensors in attitude detection system effectively, and significantly suppress the angle drift caused by the accumulated error of the gyroscope and the influence of other noises in Multi-Sensor attitude determination system.