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Nonlinear Constrained Moving Horizon Estimation Applied to Vehicle Position Estimation

Institute of System Dynamics and Control, Robotics and Mechatronics Center, German Aerospace Center (DLR), 82234 Weßling, Germany
Sensors 2019, 19(10), 2276; https://doi.org/10.3390/s19102276
Received: 21 March 2019 / Revised: 30 April 2019 / Accepted: 11 May 2019 / Published: 16 May 2019
(This article belongs to the Section Intelligent Sensors)
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. View Full-Text
Keywords: automotive applications; nonlinear observer; Kalman filter; constrained estimation; nonlinear gradient descent search; vehicle state estimation; moving horizon estimation; GNSS; IMU; INS automotive applications; nonlinear observer; Kalman filter; constrained estimation; nonlinear gradient descent search; vehicle state estimation; moving horizon estimation; GNSS; IMU; INS
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Brembeck, J. Nonlinear Constrained Moving Horizon Estimation Applied to Vehicle Position Estimation. Sensors 2019, 19, 2276.

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