Publication:
A Sensor Fusion Method Based on an Integrated Neural Network and Kalman Filter for Vehicle Roll Angle Estimation

Simple item page
dc.affiliation.areaUC3M. Área de Ingeniería Mecánicaes
dc.affiliation.dptoUC3M. Departamento de Ingeniería Mecánicaes
dc.affiliation.grupoinvUC3M. Grupo de Investigación: MECATRAN: Mecánica Experimental, Cálculo y Transporteses
dc.contributor.authorVargas Meléndez, Leandro José
dc.contributor.authorLópez Boada, Beatriz
dc.contributor.authorLópez Boada, María Jesús
dc.contributor.authorGauchía Babé, Antonio
dc.contributor.authorDíaz López, Vicente
dc.date.accessioned2016-09-16T08:02:43Z
dc.date.available2016-09-16T08:02:43Z
dc.date.issued2016-08-31
dc.description.abstractThis article presents a novel estimator based on sensor fusion, which combines the Neural Network (NN) with a Kalman filter in order to estimate the vehicle roll angle. The NN estimates a "pseudo-roll angle" through variables that are easily measured from Inertial Measurement Unit (IMU) sensors. An IMU is a device that is commonly used for vehicle motion detection, and its cost has decreased during recent years. The pseudo-roll angle is introduced in the Kalman filter in order to filter noise and minimize the variance of the norm and maximum errors' estimation. The NN has been trained for J-turn maneuvers, double lane change maneuvers and lane change maneuvers at different speeds and road friction coefficients. The proposed method takes into account the vehicle non-linearities, thus yielding good roll angle estimation. Finally, the proposed estimator has been compared with one that uses the suspension deflections to obtain the pseudo-roll angle. Experimental results show the effectiveness of the proposed NN and Kalman filter-based estimator.en
dc.description.sponsorshipThis work was possible thanks to the funds provided by the Spanish Government through the CICYTProject TRA2013-48030-C2-1-R.en
dc.format.extent18
dc.format.mimetypeapplication/pdf
dc.identifier.bibliographicCitationVargas-Meléndez, L., Boada, B.L., Boada, M.J.L., Gauchía, A., Díaz, V. (2016). A Sensor Fusion Method Based on an Integrated Neural Network and Kalman Filter for Vehicle Roll Angle Estimation. Sensors, 16 (9), 1400.
dc.identifier.doihttps://www.doi.org/10.3390/s16091400
dc.identifier.issn1424-8220
dc.identifier.publicationissue9
dc.identifier.publicationtitleSensors
dc.identifier.publicationvolume16
dc.identifier.urihttps://hdl.handle.net/10016/23582
dc.identifier.uxxiAR/0000018108
dc.language.isoeng
dc.publisherMultidisciplinary Digital Publishing Institute (MDPI)
dc.relation.projectIDGobierno de España. TRA-2013-48030-C2-1-Res
dc.relation.publisherversionhttp://dx.doi.org/10.3390/s16091400
dc.rightsAtribución 3.0 España
dc.rights.accessRightsopen access
dc.rights.urihttp://creativecommons.org/licenses/by/3.0/es/
dc.subject.ecienciaIngeniería Mecánicaes
dc.subject.otherSensor fusionen
dc.subject.otherRoll angle estimationen
dc.subject.otherNeural networken
dc.subject.otherLinear Kalman filteren
dc.titleA Sensor Fusion Method Based on an Integrated Neural Network and Kalman Filter for Vehicle Roll Angle Estimationen
dc.typeresearch article*
dc.type.hasVersionVoR*
dspace.entity.typePublication
Files
Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
sensor_S_2016.pdf
Size:
7.59 MB
Format:
Adobe Portable Document Format
Download
Collections
DIM - MECATRAN - Artículos de revistas