Title: Unscented kalman filter with process noise covariance estimation for vehicular ins/gps integration system
Abstract: The unscented Kalman filter (UKF) has proved to be a promising methodology to integrate INS and GPS for vehicular navigation. Nevertheless, the disturbance suppression of system noise uncertainty on the UKF performance is still an open issue. In this paper, based on the maximum likelihood (ML) principle, a new adaptive UKF with process noise covariance estimation is proposed to enhance the UKF robustness against process noise uncertainty for vehicular INS/GPS integration. The proposed method extends the concept of ML estimation from the linear Kalman filter to the nonlinear UKF to estimate the process noise covariance. Meanwhile, an estimation window for fixed-length memory is introduced to emphasize the use of the new observations and gradually discard the old ones. Since it has the capability to estimate and update the process noise covariance online, the proposed method improves the standard UKF by restraining the disturbance of process noise uncertainty on the filtering solution. The effectiveness and superiority of the proposed method have been verified through Monte Carlo simulations and practical experiment in vehicular INS/GPS integration system.
Publication Year: 2020
Publication Date: 2020-08-06
Language: en
Type: article
Indexed In: ['crossref']
Access and Citation
Cited By Count: 132
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