DOI QR코드

DOI QR Code

Research on PSNF-m algorithm applying track management technique

트랙관리 기법을 적용한 PSNF-m 표적추적 필터의 성능 분석 연구

  • Yoo, In-Je (Defense Agency for Technology and Quality)
  • Received : 2017.03.23
  • Accepted : 2017.06.09
  • Published : 2017.06.30

Abstract

In the clutter environment, it is necessary to update the target tracking filter by detecting the target signal among many measured value data obtained via the radar system, the track does not diverge, and tracking performance is maintained. The method of associating the measurement most relevant to the target track among numerous measurement values is referred to as data association. PSNF and PSNF-m are data association methods of SN-series. In this paper, we provide an IPSNF-m(Integrated Probabilistic Strongest Neighbor Filter-m) algorithm with a track management method based on the track existence probability in PSNF-m algorithm. This algorithm considers not only the presence of the target but also the case where the target is present but not detected. Calculating the probability of each caseenables efficient management. In order to verify the performance of the proposed IPSNF-m, the track existence probability of the IPSNF algorithm applying the track management technique to PSNF, which is known to have similar performance to PSNF-m, is derived. Through simulation in the same environment, we compare and analyze the proposed algorithm with RMSE, Confirmed True Track, and Track Existence Probability that show better performance in terms of track retention and estimation than the existing PSNF-m and IPSNF algorithms.

Keywords

Data Association;IPSNF;IPSNF-m;Target Tracking;Track Management

References

  1. Y. Bar-Shalom, T. E. Fortmann, Tracking and Data Association, Academic Press, New York, 1988.
  2. Y.Bar-Shalom, X. R. Li, Estimation and Tracking, Principles, Techniques, and Software, ArtechHouse, 1993.
  3. X. R. Li, Y. Bar-Shalom, "Tracking in Clutter with nearest neighbor filters :Analysis and Performance," IEEE Trans. on AES, vol. 32, no. 3, Jul. 1996. DOI: https://doi.org/10.1109/7.532259
  4. Li, X. R., Bar-Shalmo, Y, "Theoretical Analysis and Performance Prediction of Tracking in Clutter with Strongest Neighbor filters," The Proceedings of the 34th conference on Decision and Control, New Orleans, pp. 2758-2763, Dec. 1995
  5. Li, X. R, "Tracking in Clutter with Strongest neighbor measurements - PartI : Theoretical analysis, "IEEE Transaction on Automatic Control, vol. 43, no. 11, Nov. 1998. DOI: https://doi.org/10.1109/9.728872
  6. X. R. Lia, X. Z hi, "PSNF : A refined strongest neighbor filter for tracking in clutter, "Proceedings of the 35th CDC, Kobe Japan, pp. 2557-2562, Dec. 1996.
  7. X. Rong Li, Xiaorong Zhi, "Probabilistic Strongest Neighbor Filter for Tracking in Clutter, "Proceedings of SPIE Conference on Signal and Data Processing of Small Targets, vol. 2759, pp. 230-241, 1996. DOI: https://doi.org/10.1117/12.241211
  8. K. J. Rhee, T. L. Song,"A probabilistic strongest neighbor filter algorithm based on number of validated measurements, "Proceedings of the 16th International Sessions, JSASS, Yokohama, Oct. 2002. DOI: https://doi.org/10.1109/TAES.2009.5089532
  9. Taek Lyul Song, Kye Jin Rhee, Dong Gwan Lee, "A Probabilistic Strongest Neighbor Filter Algorithm for m Validated Measurements," The Proceedings of the 7th International Conference on Information Fusion, June 28-July1, Stockholm, Sweden, pp. 1052-1058, 2004. DOI: https://doi.org/10.1109/TAES.2009.5089532
  10. A. Papoulis, "Probability and statistics", Prentice Hall, 1990.
  11. D. Musicki, 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 https://doi.org/10.1109/TAES.2004.1309992
  12. Y.Bar-Shalom, X. R. Li, "Estimation with Applications to Tracking and Navigation", John Wiley & Sons Inc. 2001. DOI: https://doi.org/10.1002/0471221279
  13. Dong Gwan Lee, "A Study on Maneuvering Target Tracking Filters in an Adverse Environment with ECM and Clutter" pp. 11-12, Feb. 2006.
  14. Tae Han Kim, Byung In Choi, Ji Eun Kim, Yu Kyung Yang, Taek Lyul Song, "A Study of LM-IHPDA Algorithm for Multi-Target Tracking in Infrared Image Sequences", Journal of Institute of Control, Robotics and Systems, vol. 19, no. 3, pp. 209-218, Mar. 2013. DOI: https://doi.org/10.5302/J.ICROS.2013.12.1796 https://doi.org/10.5302/J.ICROS.2013.12.1796