Sensor Fusion Algorithm Based on Extended Kalman Filter for Estimation of Ground Vehicle Dynamics

• Type: Article in International Conference Proceedings Book
• Date: 2016

Authors

Barbosa, D (Author) Other; Lopes, A (Author) Other; Rui Esteves Araújo (Author) FEUP

Conference proceedings International

Title: IECON Proceedings (Industrial Electronics Conference)

Pages: 1049-1054

42nd Conference of the Industrial Electronics Society, IECON 2016

24 October 2016 through 27 October 2016

Indexing

ISI Web of Knowledge - 5 Citations

Scopus - 8 Citations

Other information

Authenticus ID: P-00M-CQT

DOI: 10.1109/iecon.2016.7793145

Abstract (EN): The current vehicle stability control techniques relies on an accurate sensor information and a complete system definition, such information is not easily obtained and requires expensive sensor technology. In this work it is presented a fusion algorithm for estimating the vehicle handling dynamic states, using inertial measurements combined with Global Positioning System (GPS) information, based on the Extended Kalman Filter algorithm (EKF). The proposed method will be able to track the state of the variable vector that includes the yaw rate, lateral velocity and longitudinal velocity of the vehicle using the information of the available sensors combined with the non-linear model of the system. In order to validate the proposed sensor fusion algorithm a simulation with a high-fidelity CarSim model is carried out and its sensors are compared with Extended Kalman Filter state variables.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 6