Probabilistic 4D trajectory prediction and conflict detection for air traffic control

In this paper, we study the problem of aircraft 4D trajectory prediction and conflict detection which is one of the key functions of the Next Generation Air Transportation System (NextGen). A stochastic linear hybrid system (SLHS) with two different discrete state transition models is proposed to describe the aircraft motion. Based on the SLHS model, a 4D trajectory prediction algorithm is proposed utilizing some prior information about the aircraft's intent. Also, a computationally efficient conflict detection algorithm is developed based on the cumulative distribution function (cdf) approximation for the quadratic form of Gaussian random variables. The performance of the proposed algorithms is validated through an illustrative air traffic scenario.

[1]  A. M. Mathai Quadratic forms in random variables , 1992 .

[2]  D. Kuonen Saddlepoint approximations for distributions of quadratic forms in normal variables , 1999 .

[3]  Inseok Hwang,et al.  Stochastic Linear Hybrid Systems: Modeling, Estimation, and Application in Air Traffic Control , 2009, IEEE Transactions on Control Systems Technology.

[4]  Dan Raphaeli,et al.  Series expansions for the distribution of noncentral indefinite quadratic forms in complex normal variables , 1995, Eighteenth Convention of Electrical and Electronics Engineers in Israel.

[5]  Weiyi Liu,et al.  Estimation algorithm for stochastic linear hybrid systems with quadratic guard conditions , 2009, Proceedings of the 48h IEEE Conference on Decision and Control (CDC) held jointly with 2009 28th Chinese Control Conference.

[6]  Y. Bar-Shalom,et al.  The interacting multiple model algorithm for systems with Markovian switching coefficients , 1988 .

[7]  G. Turin The characteristic function of Hermitian quadratic forms in complex normal variables , 1960 .

[8]  S. Rice Distribution of Quadratic Forms in Normal Random Variables—Evaluation by Numerical Integration , 1980 .

[9]  John Lygeros,et al.  A probabilistic approach to aircraft conflict detection , 2000, IEEE Trans. Intell. Transp. Syst..

[10]  Mario A. Rotea,et al.  New Algorithms for Aircraft Intent Inference and Trajectory Prediction , 2007 .

[11]  Heinz Erzberger,et al.  Conflict Probability Estimation for Free Flight , 1997 .

[12]  John Lygeros,et al.  Aircraft and weather models for probabilistic collision avoidance in air traffic control , 2002, Proceedings of the 41st IEEE Conference on Decision and Control, 2002..

[13]  James K. Kuchar,et al.  A review of conflict detection and resolution modeling methods , 2000, IEEE Trans. Intell. Transp. Syst..

[14]  Yaakov Bar-Shalom,et al.  Benchmark for radar allocation and tracking in ECM , 1998 .

[15]  Joseph Wat,et al.  In Service Demonstration of Advanced Arrival Techniques at Schiphol Airport , 2006 .

[16]  A. M. Mathai,et al.  Quadratic forms in random variables : theory and applications , 1992 .

[17]  Lee C. Yang,et al.  USING INTENT INFORMATION IN PROBABILISTIC CONFLICT ANALYSIS , 1998 .

[18]  S. Shankar Sastry,et al.  Aircraft conflict prediction in the presence of a spatially correlated wind field , 2005, IEEE Transactions on Intelligent Transportation Systems.

[19]  Inseok Hwang,et al.  Intent-Based Probabilistic Conflict Detection for the Next Generation Air Transportation System , 2008, Proceedings of the IEEE.

[20]  Thia Kirubarajan,et al.  Estimation with Applications to Tracking and Navigation: Theory, Algorithms and Software , 2001 .