Aircraft Conflict Risk Assessment Based on Particle Filter

Abstract:

Free flight is a method to resolve airspace congestion problem, but raise safety problem. In this paper, with the influence of wind and the presence of positioning error, the model of conflict detection based on particle filter algorithm is presented. According to the flight kinematic model with the influence of random factors, the target trajectory is generated. The particle filter algorithm is used for estimating the real flight trajectory. The flight collision risk probability is calculated. By simulation calculation, the conflict detection with particle filter algorism improves the accuracy of collision risk probability estimation. The results show that the particle filter conflict detection algorithm reduces the estimation and conflict detection error caused by random perturbation. The method can be applied to identify conflict in the early stage in the study of flight free flight.

• Periodical: Applied Mechanics and Materials (Volumes 602-605)
• Pages: 3416-3420
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.602-605.3416
• Online since: August 2014
• Authors: Wen Peng Zhai*, Hao Wu, Lan Ma
• Keywords: Air Transportation, Conflict Detection, Free Flight, Particle Filter, Risk Assessment

References

[1] Prandini M, Hu J H , Lygeros J , et al . A probabilistic approach to aircraft conflict detection [J]. IEEE Transaction of Intelligent Transportation Systems, Vol. 4, No. 1(2000), pp.199-209.

DOI: 10.1109/6979.898224

[2] Leege A P, Mulder M M. Novel Method for Wind Estimation Using Automatic Dependent Surveillance-Broadcast[J]. Journal of Guidance, Control, and Dynamics, Vol. 2, No. 22(1999), pp.219-228.

DOI: 10.2514/1.55833

[3] SHI L, WU R B. Multi-route mid-term conflict detection algorithm based on discretization of predicted position space[J], Signal processing, Vol. 10, No. 27(2011), pp.1520-1524.

[4] ZHANG Z N, ZUO J L. Study on Free Flight Collision Risk Based on Brownian Motion[J]. China Safety Science Journal, Vol. 8, No. 22(2012), pp.43-47.

[5] ZHAI W P, LI Y F. Research of Flight Track Generation and Conflict Detection Algorithm[J]. China Safety Science Journal, Vol. 10, No. 23(2013), pp.110-113.

[6] R.S. Schild. Rule Optimization for Airborne Aircraft Separation, PhD thesis, Vienna Technical University, 1998. Available form World Wide Web: http: /www. eos. tuwien. ac. at/Oeko/RSchild/Rulse.

[7] R. Slattery and Y. Zhao. Trajectory synthesis for air traffic automation, Journal of Guidance, Control and Dynamics, 20: 232-238, 1997. 8C. Tomlin, J. Lygeros, and S. Sastry. A game theoretic approach to controller design for hybrid systems, Proceeding of the IEEE, Vol. 7, No. 88(2000).

DOI: 10.2514/2.4056

[8] H. Erzberger, R. Paielli, D. R. Isaacson, and M. Eshow. Conflict detection and resolution in the presence of prediction error, In 1st USA/Europe Air Traffic Management R & D Seminar, Saclay, France, (1997).

[9] Eurocontrol Experimental Centre. User manual for the base of aircraft data (BADA), revision 3. 3, 2002. Available from World Wide Web: http: /www. eurocontrol. fr/projects/bada.

[10] Benjamin S G, Schwartz B E. Accuracy of ACARS Wind and Temperature Observations Determined by Collocation [J]. Weather and Forecasting, Vol. 6, No. 14(1999), pp.1032-1038.

DOI: 10.1175/1520-0434(1999)014<1032:aoawat>2.0.co;2

[11] Jackson M R, Zhao Y J. Sensitivity of Trajectory Prediction in Air Traffic Management [J]. Journal of Guidance, Control, and Dynamics, Vol. 2, No. 22(1999), pp.219-228.

DOI: 10.2514/2.4398