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dc.contributor.authorFossen, Sindre
dc.contributor.authorFossen, Thor I.
dc.date.accessioned2022-02-16T11:15:11Z
dc.date.available2022-02-16T11:15:11Z
dc.date.created2021-11-25T11:37:59Z
dc.date.issued2021
dc.identifier.issn1424-8220
dc.identifier.urihttps://hdl.handle.net/11250/2979320
dc.description.abstractSmall USVs are usually equipped with a low-cost navigation sensor suite consisting of a global navigation satellite system (GNSS) receiver and a magnetic compass. Unfortunately, the magnetic compass is highly susceptible to electromagnetic disturbances. Hence, it should not be used in safety-critical autopilot systems. A gyrocompass, however, is highly reliable, but it is too expensive for most USV systems. It is tempting to compute the heading angle by using two GNSS antennas on the same receiver. Unfortunately, for small USV systems, the distance between the antennas is very small, requiring that an RTK GNSS receiver is used. The drawback of the RTK solution is that it suffers from dropouts due to ionospheric disturbances, multipath, interference, etc. For safety-critical applications, a more robust approach is to estimate the course angle to avoid using the heading angle during path following. The main result of this article is a five-state extended Kalman filter (EKF) aided by GNSS latitude-longitude measurements for estimation of the course over ground (COG), speed over ground (SOG), and course rate. These are the primary signals needed to implement a course autopilot system onboard a USV. The proposed algorithm is computationally efficient and easy to implement since only four EKF covariance parameters must be specified. The parameters need to be calibrated for different GNSS receivers and vehicle types, but they are not sensitive to the working conditions. Another advantage of the EKF is that the autopilot does not need to use the COG and SOG measurements from the GNSS receiver, which have varying quality and reliability. It is also straightforward to add complementary sensors such as a Doppler Velocity Log (DVL) to the EKF to improve the performance further. Finally, the performance of the five-state EKF is demonstrated by experimental testing of two commercial USV systems.en_US
dc.language.isoengen_US
dc.publisherMDPIen_US
dc.rightsNavngivelse 4.0 Internasjonal*
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/deed.no*
dc.titleFive-State Extended Kalman Filter for Estimation of Speed Over Ground (SOG), Course over Ground (COG), and Course Rate of Unmanned Surface Vehicles (USVs): Experimental Resultsen_US
dc.typePeer revieweden_US
dc.typeJournal articleen_US
dc.description.versionpublishedVersionen_US
dc.source.volume21en_US
dc.source.journalSensorsen_US
dc.source.issue23en_US
dc.identifier.doihttps://doi.org/10.3390/s21237910
dc.identifier.cristin1958942
dc.relation.projectNorges forskningsråd: 223254en_US
dc.relation.projectNorges forskningsråd: 296630en_US
cristin.ispublishedtrue
cristin.fulltextoriginal
cristin.qualitycode1


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