Journal of the Korean Society for Aeronautical & Space Sciences (한국항공우주학회지)

The Korean Society for Aeronautical and Space Sciences (한국항공우주학회)
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Performance Improvement of Maneuvering Target Tracking with Radar Measurement Noise Estimation

레이더 측정 잡음 추정을 통한 기동 표적 추적 성능 향상

  • Received : 2010.03.05
  • Accepted : 2010.12.29
  • Published : 2010.12.25
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Abstract

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.

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.

Keywords

References

  1. H. A. P. Blom, “An Efficient Filter forAbruptly Changing Systems”, Proceedings of23rd Conference on Decision and Control, LasVegas, NV, December, 1984.
  2. X. Rong Li and Y. Bar-Shalom, “Designof Interacting Multiple Model Algorithm forAir Traffic Control Tracking”, IEEETransactions 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 TrafficSurveillance Using IMM/Assignment Estimators”,IEEE Transactions on Aerospace and ElectronicSystems, Vol. 35, No. 1, January, 1999. https://doi.org/10.1109/7.745696
  4. Y. Bar-Shalom, X. Rong Li, andThiagalingam Kirubarajan, Estimation withApplications to Tracking and Navigation, AWiley-Interscience Publication, 2001.
  5. W. Bolstad, Introduction to BayesianStatistics, A Wiley-Interscience Publication, 2007.
  6. L. Campo, P. Mookerjee, and Y.Bar-Shalom, “State Estimation for Systems withSojourn-Time-Dependent Markov ModelSwitching”, IEEE Transactions on AutomaticControl, Vol. 36, No. 2, February, 1991. https://doi.org/10.1109/9.67304
  7. H. S. Kim, and S. Y. Chun, “Design ofFuzzy IMM Algorithm Based on BasisSub-Models and Time-Varying Mode TransitionProbabilities”, 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 VarianceEstimation with Several Targets of Opportunity”,IEEE Transactions on Aerospace and ElectronicSystems, 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 andTechniques, YBS Publishing, 1995.
  10. Eurocontrol Standard Document for RadarSurveillance in En-Route Airspace and MajorTerminal 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 anAdvanced ATC System”, in Y. Bar-Shalom,Editor, Multitarget-Multisensor Tracking :Applications and Advances, Volume II, ArtechHouse, 1990.
  12. H. A. P. Blom, Rene A. Hogendoorn,and Bas A. van Doorn, “Design of aMultisensor Tracking System for Advanced AirTraffic Control”, in Y. Bar-Shalom, Editor,Multitarget-Multisensor Tracking : Applications andAdvances, Volume II, Artech House, 1990.
  13. I. S. Hwang, Air Traffic Surveillance andControl Using Hybrid Estimation andProtocol-Based Conflict Resolution, Ph.D.Dissertation, Department of Aeronautics andAstronautics, Stanford University, 2003.