Filtering based multi-sensor data fusion algorithm for a reliable unmanned surface vehicle navigation
Wenwen Liu, Yuanchang Liu, Richard Bucknall
Abstract
When considering the working conditions under which an unmanned surface vehicle (USV) operates, the navigational sensors, which already have inherent uncertainties, are subjected to environment influences that can affect the accuracy, security and reliability of USV navigation. To combat this, multi-sensor data fusion algorithms will be developed in this paper to deal with the raw sensor measurements from three kinds of commonly used sensors and calculate improved navigational data for USV operation in a practical environment. Unscented Kalman Filter, as an advanced filtering technology dedicated to dealing with non-linear systems, has been adopted as the underlying algorithm with the performance validated within various computer-based simulations where practical, dynamic navigational influences, such as ocean currents, provide force against the vessel’s structure, are to be considered.