Experimental comparison of Bayesian positioning methods based on multi-sensor data fusion
by Dominique Gruyer; Alain Lambert; Mathias Perrollaz; Denis Gingras
International Journal of Vehicle Autonomous Systems (IJVAS), Vol. 12, No. 1, 2014

Abstract: Localising a vehicle consists in estimating its position state by merging data from proprioceptive sensors (inertial measurement unit, gyrometer, odometer, etc.) and exteroceptive sensors (GPS sensor). A well-known solution in state estimation is provided by the Kalman filter. However, due to the presence of nonlinearities, the Kalman estimator is applicable only through some recursive variants among which the Extended Kalman filter (EKF), the Unscented Kalman Filter (UKF) and the Divided Differences of first and second order (DDI and DD2). We have compared these filters using the same experimental data. The results obtained aim to rank these approaches by their performances in terms of accuracy and consistency.

Online publication date: Mon, 27-Oct-2014

The full text of this article is only available to individual subscribers or to users at subscribing institutions.

 
Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.

Pay per view:
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.

Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Vehicle Autonomous Systems (IJVAS):
Login with your Inderscience username and password:

Username:        Password:          Forgotten your password?

Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.

If you still need assistance, please email subs@inderscience.com