4D flight trajectory prediction model based on improved Kalman filter
Wang Taob · Journal of Computer Applications · 2014
To solve the problem of too many parameters and low prediction precision in the traditional aerodynamic 4D trajectory prediction models, an Improved Kalman Filter( IKF) algorithm was proposed to estimate the 4D trajectory, which increased the accuracy of trajectory prediction through real-time estimation of system noise. First, according to the varying direction and velocity of aircraft during flight, the velocity was shifted. Then, the prediction models were set up separately by KF and IKF. Finally, by comparing the predictive deviations in X, Y and Z directions by two algorithms, the smaller one was selected. The simulation results illustrate that the deviations respectively reduce by 17. 65% and 98. 03% in X and Y directions by IKF; meanwhile, KF has higher accuracy in Z direction. Besides, according to the analysis of IKF in different time interval, within the width of protection zone of arrival procedure( 9. 46 km), the time interval could be increased to 20 s.