Using Interchangeably the Extended Kalman Filter and Geodetic Robust Adjustment Methods to Increase the Accuracy of Surface Vehicle Positioning in the Coastal Zone

Abstract

This paper presents a study to evaluate the comparative positioning accuracy of Surface Vehicle (SV) using Dead Reckoning (DR), Geodetic Least-Squares Adjustment (GLSA), Geodetic Robust Adjustment (GRA), and External Kalman Filter (EKF) methods. This involved simulating the results of navigational measurements subject to errors (including gross errors) used to position the SV swimming along a given trajectory in the vicinity of three beacons. We showed an apparent increase in the SV positioning accuracy, from approximately 9 m of Root Mean Square (RMS) obtained by DR and GLSA methods, to approximately 2 m (RMS), achieved using GRA and EKF methods. We also showed that, by interchanging GRA and EKF methods, it is still possible to increase the positioning accuracy of the SV up to 1.14 m (RMS). However, such an interchange should occur after the experimentally determined limit of the mean error of the position coordinates estimated by the GRA method has been exceeded.

Keywords

• navigation marks
• data fusion
• position accuracy
• surface vehicle positioning
• coastal navigation