GNSS attitude estimation based on adaptive Kalman filtering using phase measurement

Abstract

Attitude estimation using multi-antenna Global Navigation Satellite System (GNSS) has caught broad attention in recent years. The methods of indirectly calculating attitude parameters based on baseline vectors have been widely used. However, the accuracy of such methods needs to be further improved. An algorithm of directly estimating attitude parameters based on adaptive Kalman filtering (AKF) is proposed. The high-precision phase measurements are used and the system noise covariance matrix corresponding to Euler angles and angle rates is adaptively adjusted according to the maximum a posteriori estimation principle. To decrease the linearisation errors and speed the filtering convergence, a switching strategy is implemented. Namely, when the number of fixed ambiguities is equal to or more than three, the authors deem that the attitude estimate is sufficiently accurate, hence the initial state vector is constructed to start filtering. Otherwise, the attitude parameters are indirectly obtained. The static and simulation experiments are carried out. A comparative study is implemented with the two methods of indirectly calculating attitude parameters. The static and simulation results demonstrate that the proposed method has a competent performance in both accuracy and fixing success rate. The simulation results also show that the adaptability of the AKF method is nice.