Implementation of Translational Motion Dynamics for INS Data Fusion in DVL Outage in Underwater Navigation

Underwater navigation is generally accomplished through the data fusion of INS (Inertial Navigation System) and auxiliary sensors such as DVL (Doppler Velocity Logger) sensor. However, because of the possibility of DVL outage, alternative low-cost solutions are attractive. Among these, one is using vehicle kinetic model information extracted by the Newton-Euler equation to improve INS performance, which is called model-aided navigation. In this paper, only the vehicle translational motion dynamics are used to replace DVL in underwater navigation in DVL outage. The vehicle 3D translational dynamics has been obtained by using general Newton-Euler equations. Integrating these dynamics leads to the calculation of velocities in the body reference frame. Similar to DVL measurements, the calculated velocities (called Pseudo-DVL in this paper) are fused with INS. In this paper, a Kalman filter is used for INS/Pseudo-DVL data fusion. Field test data collected by an equipped research AUV called SUT-III is used to evaluate the proposed approach experimentally. Also, a comparison between the integrated navigation of INS-DVL and INS-Pseudo-DVL is performed and the results are provided. The results reveal that despite having low-cost MEMS IMUs, INS/Pseudo-DVL has effectively corrected standalone INS estimation. Finally it is concluded that despite slight degradation of navigation performance, an expensive DVL sensor can be replaced by using the proposed method in DVL outage.

Automated summary

This paper presents a model-aided underwater navigation method that integrates vehicle dynamics model information to achieve data fusion of INS/Pseudo-DVL for addressing DVL outage. Experimental results show that despite a slight degradation in navigation performance, the proposed method effectively replaces expensive DVL sensors.