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

정보융합 기법을 활용한 ADS-B 성능 개선

  • Cho, Taehwan (Department of Electronics Engineering, Republic of Korea Air Force Academy) ;
  • Kim, Kanghee (Department of Electronics Engineering, Inha University) ;
  • Kim, inhyuk (Department of Electronics Engineering, Inha University) ;
  • Choi, Sangbang (Department of Electronics Engineering, Inha University)
  • 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.

본 논문에서는 차세대 항행시스템인 ADS-B (automatic dependent surveillance - broadcast)의 성능 개선을 위해, 정보융합 기법을 제안하였다. ADS-B는 기존의 레이더에 비해 성능이 우수하지만, GNSS (global navigation satellite system) 정보에 의존하기 때문에 GNSS가 가지는 오차가 ADS-B 데이터에 포함된다. 본 논문에서는 이런 오차를 극복하기 위해 ADS-B의 데이터에 MLAT(multilateration)와 WAM (wide area multilateration)의 데이터를 융합하여 ADS-B의 성능을 개선하였다. 정보융합을 하면 기존의 데이터에 비해 정확성이 우수한 데이터를 얻을 수 있다. 기존의 정보융합 기법은 부 필터로 칼만필터나 IMM 필터를 사용하지만, 제안된 기법에서는 Robust IMM 필터를 사용하여 항공기 위치추적 성능을 향상시켰다. 또한 실제 ADS-B 데이터를 사용하여 시뮬레이션 결과 대비 신뢰도를 높였다.

Keywords

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