Application of Modified EKF Based on Intelligent Data Fusion in AUV Navigation

Effective and accurate navigation is critical for Autonomous Underwater Vehicle (AUV). Extended Kalman Filter (EKF) is widely used in AUV navigation system to ensure precise localization. Nevertheless, unknown deviation from low-cost sensors such as Attitude and Heading Reference System (AHRS) and Doppler velocity log (DVL) may have an adverse impact on AUV navigation. This paper proposed an intelligent data fusion method utilizing deep neural networks to modify the EKF to compensate for the deviation. This proposed approach has been tested on the experimental datasets acquired by our own research platform, Sailfish AUV-210, during lake trials at Menlou Reservoir and sea trials at Tuandao Bay. The results indicated that the performance of presented algorithm is significantly superior to EKF and even better than Unscented Kalman Filter (UKF), revealing that the proposed algorithm successfully improving the accuracy of navigation system.