An Improved Sage-Husa Adaptive Kalman Filtering Applied to Cooperative Navigation of Autonomous Underwater Vehicles

In this paper, an adaptive Kalman filtering based on the improved Sage-Husa is proposed to improve the stability and accuracy in cooperative navigation. In the master-slave AUV cooperative navigation system, the proposed improved Sage-Husa algorithm adopts the sequential approach to ensure the positive definiteness of the covariance matrix measured in the filtering process. The proposed algorithm also reduces the divergence of the positioning filtering by introducing fading factor adaptively. Compared to the original algorithm, the simulation results of the cooperative navigation system show that the proposed algorithm has better adaptability and stability in case of uncertain abnormal measurements.