Estimation of the Real Vehicle Velocity Based on UKF and PSO

Paper #:
  • 2014-01-0107

Published:
  • 2014-04-01
Citation:
Zhuo, G. and Zhang, F., "Estimation of the Real Vehicle Velocity Based on UKF and PSO," SAE Technical Paper 2014-01-0107, 2014, https://doi.org/10.4271/2014-01-0107.
Pages:
5
Abstract:
The unscented Kalman filter (UKF) is applied to estimate the real vehicle velocity. The velocity estimation algorithm uses lateral acceleration, longitudinal acceleration and yaw rate as inputs. The non-linear vehicle model and Dugoff tire model are built as the estimation model of UKF. Some parameters of Dugoff tire model and vehicle, which can't be measured directly, are identified by the particle swarm optimization (PSO). For the purpose of evaluating the algorithm, the estimation values of UKF are compared with measurements of the Inertial and GPS Navigation system. Besides, the real time property of UKF is tested by xPC Target, which is a real-time software environment from MathWorks. The result of the real vehicle experiment demonstrates the availability of the UKF and PSO in vehicle velocity estimation.
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