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Sensor Fault Detection and Signal Improvement using Predictive Filters

Article dans une revue avec comité de lecture
Author
KERHASCOET, Hugo
491393 Lab-STICC_UBS_CACS_MOCS
MERIEN, P
LAURENT, Johann
491393 Lab-STICC_UBS_CACS_MOCS
SENN, Eric
491393 Lab-STICC_UBS_CACS_MOCS
ccHAUVILLE, Frederic
13094 Institut de Recherche de l'Ecole Navale [IRENAV]

URI
http://hdl.handle.net/10985/15041
DOI
10.3940/rina.ijsct.2017.b1.186
Date
2017
Journal
The transaction of the Royal Institution of Naval Architects

Abstract

Navigation systems used in racing boats require sensors to be more and more sophisticated in order to obtain accurate information in real time. To meet the need for accuracy of the surface speed measurement, the mechanical sensor paddle wheel has been replaced by the ultrasonic sensor. This ultrasonic sensor measures the water speed precisely and with very good linearity. Furthermore, by its principle of operation, it measures the water flow several centimetres from the sensor, which puts it outside the boundary layer, the region close to the hull where the flow is disturbed. However, this sensor has several drawbacks: it is quite sensitive and if the flow contains too many air bubbles, the sensor picks them up, which can happen quite frequently on boat with a planing hull. Another limitation of this sensor is its low frequency measurement rate. In this paper, we explain the techniques used based on Kalman filters to address these shortcomings, firstly by identifying the inaccurate measurements caused by inadvertent dropouts, then by improving the useful sensor frequency with GNSS data fusion.

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