DOI QR코드

DOI QR Code

레이더 측정 잡음 추정을 통한 기동 표적 추적 성능 향상

Performance Improvement of Maneuvering Target Tracking with Radar Measurement Noise Estimation

  • 투고 : 2010.03.05
  • 심사 : 2010.12.29
  • 발행 : 2010.12.25

초록

항공관제용 감시자료 처리시스템에 의한 기동 표적 추적에 있어서 레이더의 측정 잡음 분산은 상태 추정기의 입력으로서, 추적 정확도에 영향을 주는 주요한 요소 중 하나이다. 본 연구에서는 레이더의 측정 잡음 분산을 상수가 아닌 변수로 지정하여, 다중 IMM 필터의 우도함수를 통해 매 시간 측정 잡음 분산을 실시간으로 추정하는 알고리즘을 제시하였다. Monte Carlo 시뮬레이션 결과 측정 잡음 분산 값을 실제 값 대비 5% 이내 수준으로 예측함을 확인하였고, 이를 통해 기동 표적 추적 성능을 향상시킬 수 있음을 확인하였다.

Measurement noise variance of the radar is one of the main inputs of a state estimator of surveillance data processing system for air traffic control and has influences on the accuracy performance of maneuvering target tracking. A method is presented of estimating measurement noise variances every frame of target tracking using likelihood functions of multiple IMM filter. The results by running of Monte Carlo simulation show that variances are estimated within 5% of errors compared with true values and the tracking accuracy performance is improved.

키워드

참고문헌

  1. H. A. P. Blom, “An Efficient Filter for Abruptly Changing Systems”, Proceedings of 23rd Conference on Decision and Control, Las Vegas, NV, December, 1984.
  2. X. Rong Li and Y. Bar-Shalom, “Design of Interacting Multiple Model Algorithm for Air Traffic Control Tracking”, IEEE Transactions on Control Systems Technology, Vol. 1, No. 3, September, 1993. https://doi.org/10.1109/87.251886
  3. H. Wang, T. Kirubarajan, and Y. Bar-Shalom, “Precision Large Scale Air Traffic Surveillance Using IMM/Assignment Estimators”, IEEE Transactions on Aerospace and Electronic Systems, Vol. 35, No. 1, January, 1999. https://doi.org/10.1109/7.745696
  4. Y. Bar-Shalom, X. Rong Li, and Thiagalingam Kirubarajan, Estimation with Applications to Tracking and Navigation, A Wiley-Interscience Publication, 2001.
  5. W. Bolstad, Introduction to Bayesian Statistics, A Wiley-Interscience Publication, 2007.
  6. L. Campo, P. Mookerjee, and Y. Bar-Shalom, “State Estimation for Systems with Sojourn-Time-Dependent Markov Model Switching”, IEEE Transactions on Automatic Control, Vol. 36, No. 2, February, 1991. https://doi.org/10.1109/9.67304
  7. H. S. Kim, and S. Y. Chun, “Design of Fuzzy IMM Algorithm Based on Basis Sub-Models and Time-Varying Mode Transition Probabilities”, International Journal of Control, Automation, and Systems, Vol. 4, No. 5, 2006.
  8. R. W. Osborne, III, Y. Bar-Shalom, and T. Kirubarajan, “Radar Measurement Noise Variance Estimation with Several Targets of Opportunity”, IEEE Transactions on Aerospace and Electronic Systems, Vol. 44, No. 3, July, 2008. https://doi.org/10.1109/TAES.2008.4655358
  9. Y. Bar-Shalom and X. Rong Li, Multitarget-Multisensor Tracking : Principles and Techniques, YBS Publishing, 1995.
  10. Eurocontrol Standard Document for Radar Surveillance in En-Route Airspace and Major Terminal Areas, SUR.ET1.ST01.1000-STD-01-01, Eurocontrol, March, 1997.
  11. P. Vacher, I. Barret, and M. Gauvrit., “Design of a Tracking Algorithm for an Advanced ATC System”, in Y. Bar-Shalom, Editor, Multitarget-Multisensor Tracking : Applications and Advances, Volume II, Artech House, 1990.
  12. H. A. P. Blom, Rene A. Hogendoorn, and Bas A. van Doorn, “Design of a Multisensor Tracking System for Advanced Air Traffic Control”, in Y. Bar-Shalom, Editor, Multitarget-Multisensor Tracking : Applications and Advances, Volume II, Artech House, 1990.
  13. I. S. Hwang, Air Traffic Surveillance and Control Using Hybrid Estimation and Protocol-Based Conflict Resolution, Ph.D. Dissertation, Department of Aeronautics and Astronautics, Stanford University, 2003.