Vehicle position estimation using GPS/CAN data based on nonlinear programming

0361020 - ÚTIA 2012 RIV GB eng C - Conference Paper (international conference)
Pavelková, Lenka
Vehicle position estimation using GPS/CAN data based on nonlinear programming.
Proceedings of the 13th IASTED International Conference on Intelligent Systems and Control. Cambridge: IASTED, 2011 - (Whidborne, J.; Willis, P.), s. 208-215. ISBN 978-0-88986-889-2.
[13th IASTED International Conference on Intelligent Systems and Control. Cambridge (GB), 11.07.2011-13.07.2011]
R&D Projects: GA MŠMT(CZ) 1M0572; GA ČR GA102/08/0567
Institutional research plan: CEZ:AV0Z10750506
Keywords : nonlinear state-space model * state filtering * incomplete data * bounded noise * vehicle position estimation
Subject RIV: BC - Control Systems Theory
http://library.utia.cas.cz/separaty/2011/AS/pavelkova-vehicle position estimation using gps-can data based on nonlinear programming.pdf

The paper solves a problem of the estimation of the moving vehicle position. The position is measured by global position system (GPS) but outages sometimes occur in the measurements. During these outages, the actual position is estimated using data from vehicle sensors. A moving vehicle is described by a discrete-time state-space model with bounded noise. This model is constructed using kinematics laws and it can be used for arbitrary type of ground vehicle. Bayesian approach is applied to obtain position estimates. The maximum a posteriori (MAP) estimation converts to the nonlinear programming. The paper also discusses a setting of initial conditions for successful running of estimation process.
Permanent Link: http://hdl.handle.net/11104/0198436