A new adaptive Kalman filter for navigation systems of carrier-based aircraft

The features of carrier-based aircraft's navigation systems during the approach and land -ing phases are investigated. A new adaptive Kalman filter with unknown state noise statistics is pro-posed to improve the accuracy of the INS/GNSS integrated navigation system. The adaptive filtering algorithm aims to estimate and adapt the unknown state noise covariance Q in high dynamic conditions, when the measurement noise covariance R is assumed to be known empirically in advance. The new adaptive Kalman filter based on the innovation sequence and pseudo-measurement vector approach makes it more effective to estimate and adapt Q. The simulation results and semi-physical experiments show that the application of the proposed adaptive Kalman filter can guarantee a higher estimation accuracy of the state variables.(c) 2021 Chinese Society of Aeronautics and Astronautics. Production and hosting by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

Automated summary

This study investigates the features of carrier-based aircraft's navigation systems during the approach and landing phases. A new adaptive Kalman filter is proposed to improve the accuracy of the INS/GNSS integrated navigation system by considering unknown state noise covariance Q. The results of simulations and semi-physical experiments demonstrate that the application of the proposed adaptive Kalman filter can ensure higher estimation accuracy of the state variables.