Aircraft Tracking and 4D Trajectory Prediction

Our group develops a set of aircraft tracking and four-dimensional trajectory prediction algorithms based on the Stochastic Hybrid System (SHS) modeling and hybrid state estimation algorithms.

Stochastic Linear Hybrid System (SLHS) model / Residual-Mean Interacting Multiple Model (RMIMM) algorithm [1]
SLHS model / RMIMM for multiple-target tracking [2]
SLHS model / RMIMM with intent inference: Intent-based Trajectory Prediction
SLHS model / probabilistic reachability analysis: Probabilistic Trajectory Prediction and Conflict Detection
SLHS with state-dependent-transition / State-Dependent-Transition Hybrid Estimation (SDTHE) algorithm [3], [4]
Nonlinear SHS with state-dependent-transition / SDTHE-based algorithm: Estimated Time of Arrival (ETA) Prediction in Terminal Airspace

Arrival aircraft trajectories in the horizontal plane [4]

Aircraft trajectories for a descent phase [3,4]

Trajectories of multiple aircraft with clutter [2]

Trajectory of an aircraft along a Continuous Descent Approach (CDA) procedure [ETA prediction]

I. Hwang, H. Balakrishnan, and C. Tomlin, “State Estimation for Hybrid Systems: Applications to Aircraft Tracking,” IEEE Proceedings of Control Theory and Applications, Vol. 153(5): 556-566, September 2006
I. Hwang, H. Balakrishnan, K. Roy, and C. Tomlin, “Multiple-Target Tracking and Identity Management Algorithm with Application to Aircraft Tracking,” AIAA Journal of Guidance, Control and Dynamics, Vol. 30(3): 641-653, May-June 2007
C.-E. Seah and I. Hwang, “Stochastic Linear Hybrid Systems: Model, Estimation, and Application to Aircraft Tracking,” IEEE Transactions on Control Systems Technology, Vol. 17(3), May 2009
C.-E. Seah and I. Hwang, “Terminal-Area Aircraft Tracking by Hybrid Estimation,” AIAA Journal of Guidance, Control and Dynamics, Vol. 32 (3), p836-849, May-June 2009