Position estimation for underwater vehicles using unscented Kalman filter with Gaussian process prediction

The present paper explores the use of Gaussian processunscented Kalman filter (GP-UKF) algorithm for position estimation of underwater vehicles. GP-UKF has a number of advantages over parametric unscented Kalman filters (UKFs) and Bayesian filters, such as improved tracking quality and graceful degradation with the increase of model uncertainty. The advantage of Gaussian process (GP) over parametric models is that GP considers noise and uncertainty in model identification. These qualities are highly desired for underwater vehicles as the number and quality of sensors available for position estimation are limited. The application of non-parametric models on navigation for underwater vehicles can lead to faster deployment of the platform, reduced costs and better performance than parametric methodologies. In the present study, a REMUS 100 parametric model was employed for the generation of data and internal model in the calculation to compare the performance of an ideal UKF against GP-UKF for position estimation. GP-UKF demonstrated better performance and robustness in the estimation of vehicle position and state correction compared to the ideal UKF