Integration of nonlinear observer and unscented Kalman filter for pose estimation in autonomous truck–trailer and container truck

This paper introduces a new approach to state estimation called nonlinear observer-unscented Kalman filter (NLO-UKF). The proposed method is designed to improve the accuracy of state estimation in complex systems that are subject to nonlinearity and uncertainty. The key idea of the NLO-UKF is to use a nonlinear observer to correct the projected sigma points based on a measurement, and then update the mean and covariance using the UKF. The paper provides a detailed description of the NLO-UKF algorithm and demonstrates its boundedness. The use of NLO-UKF for pose estimation is presented to compare the effectiveness of the proposed method with other state estimation methods in the simulation of an autonomous truck–trailer system and experimentation with a container truck system. The NLO-UKF demonstrates improved accuracy during steady-state estimation.