Road vehicle state estimation using low-cost GPS/INS

Abstract

Assuming known vehicle parameters, this paper proposes an innovative integrated Kalman filter (IKF) scheme to estimate vehicle dynamics, in particular the sideslip, the heading and the longitudinal velocity. The IKF is compared with the 2DoF linear bicycle model, the triple Kalman filter (KF) and a model-based KF (MKF) in a simulation environment. Simulation results show that the proposed IKF is superior to other KF designs (both Kinematic KF and MKF) on state estimation when tyre characteristics are within the linear region (i.e. manoeuvres below 55 kph).