Target tracking and Estimated Time of Arrival (ETA) Prediction for Arrival Aircraft

The problem of developing a unified algorithm for arrival aircraft target tracking and Estimated Time of Arrival (ETA) prediction is approached from a hybrid linear systems approach. Discrete-time hybrid state models are derived and two state estimation algorithms, the Interacting Multiple Model (IMM) and particle filtering with resampling, are implemented for target tracking. Along with the standard Markov chain model for discrete mode changes, the idea of autonomous transitions, or mode changes which depend on the continuous state, are utilized in filtering. The IMM algorithm with autonomous transitions incorporated in discrete mode estimation is developed as an effective ETA predictor. The IMM algorithm is also found to be more efficient than particle filters in terms of run-time and target tracking accuracy. Tracking is performed on observed and simulated data to have RMS errors of less than 50 ft in position and less than 10 ft/s in velocity. ETA predictions are made within 30 seconds of actual landing time for time horizons of nearly 20 minutes.