Near real time improved UUV positioning through channel estimation the Unscented Kalman Filter approach
The primary objective of this work is to enhance the navigational and positioning accuracy of Unmanned Underwater Vehicles (UUVs) by networking a number of Unmanned Surface Vehicles (USVs) utilizing underwater acoustic modems and acoustic travel time calculations. In a previous work [1] a tracking algorithm based on the Extended Kalman Filter (EKF) was developed presenting satisfactory results, but the assumption of the errors being Gaussian and zero mean at the base of the EKF is violated by the presence of nonlinearities in the measurement equation. In this work, a more suitable approach based on the Unscented Kalman Filter (UKF) is presented and its results compared to the existing approach. A combination of the EKF/UKF with a Smoothing Algorithm was developed and extensively tested with synthetic data. To validate the concepts, the tracking algorithms (EKF and UKF based) were applied to data collected during sea tests that took place in Monterey Bay in August, 2015.
OCEANS 2016 MTS/IEEE Monterey