An Integrated Navigation Algorithm with Asynchronous Fusion forFull-Ocean-Depth Human Occupied Vehicle

The asynchronous fusion maybe happen in the integrated navigation of full-ocean-depth human occupied vehicle (HOV), and large error can be caused if the traditional integrated navigation algorithms are used. To solve this problem, an integrated navigation algorithm with asynchronous fusion is proposed based on machine learning (ML) and unscented Kalman filter (UKF). At first, an ML model is established for prediction of the ultra-short baseline(USBL) acoustic positioning system. Then, the model is trained by the observation dataset of USBL acoustic positioning system, and the data in the intervals between updates are predicted by the model. Finally, the updated dataset is fused by using UKF. The results of simulation experiments manifest that compared with the traditional integrated navigation algorithms, the error caused by asynchronous data from USBL acoustic positioning system can be reduced by 17%, by the proposed integrated navigation algorithm with asynchronous fusion, and the accuracy of the whole integrated navigation system is effectively improved.