Velocity Aided, Correlated Noise Extended Kalman Filtering for Attitude Estimation: a Motorcycle Case Study

Abstract

Vehicle attitude estimation is nowadays essential for a wide range of applications, e.g. guidance of unmanned vehicles, robotics and automotive controls. In this paper, the attitude estimation problem is solved by means of a velocity-aided, Extended Kalman Filter with correlated noise (CEKF), exploiting the intrinsic correlation between sensor noise in a velocity-aided model. Reconstruction of a motorcycle attitude is considered as a use case. The problem is addressed in a simulation scenario under noisy measurements. Performance of the CEKF is compared to that of the classic EKF formulation by evaluating the RMS of the reconstruction error.