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Nonlinear Constrained Moving Horizon Estimation Applied to Vehicle Position Estimation

Brembeck, Jonathan (2019) Nonlinear Constrained Moving Horizon Estimation Applied to Vehicle Position Estimation. Sensors, 19 (10), p. 2276. Multidisciplinary Digital Publishing Institute (MDPI). DOI: 10.3390/s19102276 ISSN 1424-8220

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Official URL: https://www.mdpi.com/1424-8220/19/10/2276

Abstract

The design of high–performance state estimators for future autonomous vehicles constitutes a challenging task, because of the rising complexity and demand for operational safety. In this application, a vehicle state observer with a focus on the estimation of the quantities position, yaw angle, velocity, and yaw rate, which are necessary for a path following control for an autonomous vehicle, is discussed. The synthesis of the vehicle’s observer model is a trade-off between modelling complexity and performance. To cope with the vehicle still stand situations, the framework provides an automatic event handling functionality. Moreover, by means of an efficient root search algorithm, map-based information on the current road boundaries can be determined. An extended moving horizon state estimation algorithm enables the incorporation of delayed low bandwidth Global Navigation Satellite System (GNSS) measurements—including out of sequence measurements—as well as the possibility to limit the vehicle position change through the knowledge of the road boundaries. Finally, different moving horizon observer configurations are assessed in a comprehensive case study, which are compared to a conventional extended Kalman filter. These rely on real-world experiment data from vehicle testdrive experiments, which show very promising results for the proposed approach.

Item URL in elib:https://elib.dlr.de/127735/
Document Type:Article
Title:Nonlinear Constrained Moving Horizon Estimation Applied to Vehicle Position Estimation
Authors:
AuthorsInstitution or Email of AuthorsAuthors ORCID iD
Brembeck, Jonathanjonathan.brembeck (at) dlr.deUNSPECIFIED
Date:2019
Journal or Publication Title:Sensors
Refereed publication:Yes
Open Access:Yes
Gold Open Access:Yes
In SCOPUS:Yes
In ISI Web of Science:Yes
Volume:19
DOI :10.3390/s19102276
Page Range:p. 2276
Publisher:Multidisciplinary Digital Publishing Institute (MDPI)
ISSN:1424-8220
Status:Published
Keywords:automotive applications; nonlinear observer; Kalman filter; constrained estimation; nonlinear gradient descent search; vehicle state estimation; moving horizon estimation; GNSS; IMU; INS
HGF - Research field:Aeronautics, Space and Transport
HGF - Program:Transport
HGF - Program Themes:Road Transport
DLR - Research area:Transport
DLR - Program:V ST Straßenverkehr
DLR - Research theme (Project):V - NGC KoFiF
Location: Oberpfaffenhofen
Institutes and Institutions:Institute of System Dynamics and Control > Vehicle System Dynamics
Deposited By: Klauer, Monika
Deposited On:11 Jun 2019 15:44
Last Modified:14 Dec 2019 04:27

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