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

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 m...

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Bibliographic Details
Published in:Applied sciences 2023-02, Vol.13 (4), p.2110
Main Authors: Naus, Krzysztof, Szymak, Piotr
Format: Article
Language:English
Subjects:
Accuracy
Algorithms
Cameras
coastal navigation
Coastal zone
data fusion
Dead reckoning
Errors
Extended Kalman filter
Methods
navigation marks
Normal distribution
position accuracy
Position measurement
Sensors
surface vehicle positioning
Surface vehicles
Swimming
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Summary: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.
ISSN:2076-3417
2076-3417
DOI:10.3390/app13042110