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The Performance Enhancement of Automatic Dependent Surveillance - Broadcast Using Information Fusion Method

정보융합 기법을 활용한 ADS-B 성능 개선

  • Received : 2015.08.07
  • Accepted : 2015.10.14
  • Published : 2015.10.30

Abstract

In this paper, we proposed an information fusion method for enhancement of automatic dependent surveillance - broadcast (ADS-B) system which is one of the next generation navigation system. Although ADS-B provides better performance than traditional radar, ADS-B still has error due to dependence of global navigation satellite system (GNSS) information. In this paper, we improved the ADS-B performance using information fusion of multilateration (MLAT) and wide area multilateration (WAM). Information fusion provides accurate data compared to original data. Mostly, information fusion methods use Kalman filter or IMM(interacting multiple model) filter as a subfilter. However, we used Robust IMM filter as a subfilter to improve the aircraft tracking performance. Also, we use actual ADS-B data not virtual data to increase reliability of our information fusion method.

Keywords

Information fusion;Automatic dependent surveillance - broadcast;Multilateration;Wide area multilateration

References

  1. Eurocontrol, CAT021, ADS-B messages, 2003.
  2. SRA international, Multilateration & ADS-B executive reference guide, 2009.
  3. Eurocontrol, Generic safety assessment for ATC surveillance using wide area multilateration, 2008.
  4. Y. Gao, E. J. Krakiwsky, M. A. Abousalem, and J. F. McLellan, "Comparison and analysis of centralized, decentralized, and federated filters," Journal of Institute of Navigation, Vol. 40, pp. 69-86, 1993. https://doi.org/10.1002/j.2161-4296.1993.tb02295.x
  5. H. Wang, T. Kirubarajan, and Y. Bar-Shalom, "Precision large scale air traffic surveillance using IMM/assignment estimators," IEEE Transactions of Aerospace and Electronic Systems, Vol. 35, No. 1, pp. 255-266, 1999. https://doi.org/10.1109/7.745696
  6. M. Yeddanapudi, Y. Bar-Shalom, and K. Pattipati, "IMM Estimation for multitarget-multisensor air traffic surveillance," in Proceedings of the 34th IEEE Conference on Decision and Control, New Orleans: LA, 1995.
  7. X. R. Li, W. Wang, M. Logan, and T. Donohue, "Multiplatform multisensor fusion with adaptive-rate data communication," IEEE Transactions of Aerospace and Electronic Systems, Vol. 33, No. 1, pp. 274-281, 1997. https://doi.org/10.1109/7.570781
  8. B. Olivier, H. Nicolas, and T. Olivier, "Radar/ADS-B data fusion architecture for experimentation purpose," in Proceedings of the 9th International Conference on Information Fusion, Florence: Italy, 2006.
  9. H. Durrant-whyte and T. C. Henderson, Multisensor Data Fusion, Springer Handbook of Robotics, New York, NY: Springer, 2008.
  10. Federal aviation administration, Surveillance and broadcast services integration into ATC automation processing requirements document, 2008.
  11. Federal aviation administration, Fusion, 2007.
  12. T. G. Lee, "Centralized Kalman filter with adaptive measurement fusion: Its application to a GPS/SDINS integration system with an additional sensor," International Journal of Control and Automation System, Vol. 4, pp. 444-452, 2003.
  13. D. Smith and S. Singh, "Approaches to multisensor data fusion in target tracking: A Survey," IEEE Transactions on Knowledge and Data Engineering, Vol. 18, No. 12, pp. 1696-1711, 2006. https://doi.org/10.1109/TKDE.2006.183
  14. T. H. Cho, J. H. Kim, and S. B. Choi, "Robust filtering algorithm for improvement of air navigation system," The Journal of Korea Navigation Institute, Vol. 19, No. 2, pp. 123-132, 2015. https://doi.org/10.12673/jant.2015.19.2.123