Journal of the Korea Academia-Industrial cooperation Society (한국산학기술학회논문지)

The Korea Academia-Industrial cooperation Society (한국산학기술학회)
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Research on improvement of target tracking performance of LM-IPDAF through improvement of clutter density estimation method

클러터밀도 추정 방법 개선을 통한 LM-IPDAF의 표적 추적 성능 향상 연구

  • Received : 2017.02.09
  • Accepted : 2017.05.12
  • Published : 2017.05.31
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Abstract

Improving tracking performance by estimating the status of multiple targets using radar is important. In a clutter environment, a joint event occurs between the track and measurement in multiple target tracking using a tracking filter. As the number increases, the joint event increases exponentially. The problem to be considered when multiple target tracking filter design in such environments is that first, the tracking filter minimizes the rate of false track alarmsby eliminating the false track and quickly confirming the target track. The purpose is to increase the FTD performance. The second consideration is to improve the track maintenance performance by allocating each measurement to a track efficiently when an event occurs. Through two considerations, a single target tracking data association technique is extended to a multiple target tracking filter, and representative algorithms are JIPDAF and LM-IPDAF. In this study, a probabilistic evaluation of many hypotheses in the assignment of measurements was not performed, so that the computation amount does not increase nonlinearly according to the number of measurements and tracks, and the track existence probability based on the track density The LM-IPDAF algorithm was introduced. This paper also proposes a method to reduce the computational complexity by improving the clutter density estimation method for calculating the track existence probability of LM-IPDAF. The performance was verified by a comparison with the existing algorithm through simulation. As a result, it was possible to reduce the simulation processing time by approximately 20% while achieving equivalent performance on the position RMSE and Confirmed True Track.

레이다를 이용한 다수 표적의 상태 추정을 통해 추적 성능을 향상시키는 문제는 중요하다. 클러터 환경에서 추적 필터를 이용하여 다수 표적 추적 시 트랙과 측정치 간의 결합사건이 발생하며 개수가 증가함에 따라 결합사건은 기하급수적으로 증가한다. 이러한 환경에서 다수 표적 추적 필터 설계 시 고려해야할 문제는 첫째, 신속한 거짓트랙 제거 및 표적트랙 확정을 통하여 오경보율 최소화하고, 이를 통해 FTD(False Track Discrimination) 성능을 높인다. 둘째, 다수의 트랙이 측정치를 공유하는 결합사건 발생시 효율적으로 각각의 측정치를 트랙에 할당함으로써 트랙 유지성능을 향상시키는 것이다. 두 가지 고려사항을 통해 단일 표적 추적 자료결합 기법을 다수 표적 추적 필터로 확장하여 사용하며, 대표적인 알고리듬으로 JIPDAF(Joint Integrated Probabilistic Data Association Filter)와 LM-IPDAF(Linear Multi-target IPDAF)가 있다. 본 논문에서는 측정치 할당 시 생기는 수 많은 가설들에 대한 확률적 평가를 하지 않음으로써 측정치와 트랙의 개수에 따라 비선형으로 연산량이 증가하지 않으며, 클러터밀도 추정을 통해 트랙을 쇄신하는 트랙존재확률 기반의 LM-IPDAF 알고리듬을 소개한다. 그리고 LM-IPDAF의 트랙존재확률 산출 시 필요한 클러터밀도 추정 방법을 개선함으로써 연산량을 효과적으로 감소시킬 수 있는 방법을 제안하고 시뮬레이션을 통해 기존의 알고리듬과 비교, 분석하여 성능을 검증하였다. 그 결과, 위치 RMSE, Confirmed True Track 측면에서는 동일한 성능을 내면서 시뮬레이션 처리 시간을 약 20% 감소시킬 수 있었다.

Keywords

References

  1. Y. Bar-Shalom and T. E. Fortmann, Tracking and Data Association, Academic Press, New York, 1988.
  2. Y.Bar-Shalom and X. R. Li, Estimation and Tracking, Principles, Techniques, and Software, ArtechHouse, 1993.
  3. Y. Bar-Shalom and X. R. Li, Multitarget - multisensor Tracking, Principles and Techniques, Storrs, CT : YBS Publishing, 1995.
  4. Y.Bar-Shalom, X. R. Li, and Kirubarajan, Estimation with Applications to Tracking and Navigation, Wiley, New York, 2001.
  5. D. Musicki, R. Evans and S.S tankovic, "Integrated Probabilistic Data Association (IPDA)", Proceedings of the 31st Conference on Decision and Control, Tucson, Artzone, Dec. 1992.
  6. D. Musicki, R. Evans, "Integrated Probabilistic Data Association in Clutter with Finite Resolution Sensor," Proceedings of the 32nd Conference on Decision and Control, San Astonlo, Texas, Dec. 1993. DOI: https://doi.org/10.1109/cdc.1993.325012
  7. D. Musicki, R. Evans, and S. Stankovic, "Integrated Probabilistic Data Association," IEEE Transactions on Automatic Control, vol. 39, no. 6, pp. 1237-1241, Jun. 1994. DOI: https://doi.org/10.1109/9.293185
  8. D. Musicki, R. Evans, "Joint integrated probabilistic data association : JIPDA", IEEE Transactions on Aerospace and Electronic Systems, vol. 40, no. 3, pp. 1093-1099, Jul. 2004. DOI: https://doi.org/10.1109/TAES.2004.1337482
  9. D. Musicki, B. Scala, "Multi-Target Tracking in Clutter without Measurement Assignment", IEEE Transactions on Aerospace and Electronic Systems, vol. 44, no. 3, pp. 877-896, Jul. 2008. DOI: https://doi.org/10.1109/TAES.2008.4655350
  10. X. R. Li and Y. Bar-Shalom, "Tracking in Clutter with nearest neighbor filters :Analysis and Performance," IEEE Trans. AES, vol. 32, no. 3, pp. 995-1010, Jul. 1996. DOI: https://doi.org/10.1109/7.532259
  11. X. Rong Li, "The PDF of Nearest Neighbor Measurement and A Probabilistic Nearest Neighbor Filter for Tracking in Clutter," The Proceedings of the 32nd Conference on Decision and Control, San Antonio, Texas, pp.918-923, Dec. 1993.
  12. Song, T. L., Lee, D. G. and Ryu, J. h, "A probabilistic nearest neighbor filter algorithm for tracking in clutter Environment," Signal Processing, vol. 85, Issue10, pp. 2044-2053, Oct. 2005. DOI: https://doi.org/10.1016/j.sigpro.2005.01.016
  13. Song, T. L. and Shin, S. J., "A Probabilistic Nearest Neighbor Filter for m validated measurements," Proceedings of the 6th International Conference on Information Fusion, Carins, Australia, 7. 2003.
  14. Song, T. L. and Lee, D. G.,"A Probabilistic Nearest Neighbor Filter Algorithm for m Validated Measurements," IEEE Transaction on Signal Processing, vol. 54, no. 7, pp. 2797-2802, July. 2006. DOI: https://doi.org/10.1109/TSP.2006.874803
  15. X. Rong Li, Probability, Random Signals, and Statistics, CRC, 1999.
  16. Athanasios Papoulis, S.Unnikrishna Pillai, Probability, Random Variables and Stochastic Processes Fourth edition, McGraw Hill, 2002.
  17. D. Musicki and R. J. Evans, "Clutter map information for data association and track initialization," IEEE Trans. of Aerospace Electronic Systems, vol. 40, no. 2, pp. 387-398, Apr. 2004. DOI: https://doi.org/10.1109/TAES.2004.1309992