A new adaptive Kalman filter for navigation systems of carrier-based aircraft
Lifei Zhang, Shaoping Wang, M. S. Selezneva, Konstantin A. Neusypin
Abstract
The features of carrier-based aircraft’s navigation systems during the approach and landing phases are investigated. A new adaptive Kalman filter with unknown state noise statistics is proposed 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.