The Journal of the Astronautical Sciences

, Volume 63, Issue 4, pp 308–334 | Cite as

Minimum Uncertainty JPDA Filters and Coalescence Avoidance for Multiple Object Tracking

Article

Abstract

Two variations of the joint probabilistic data association filter (JPDAF) are derived and simulated in various cases in this paper. First, an analytic solution for an optimal gain that minimizes posterior estimate uncertainty is derived, referred to as the minimum uncertainty JPDAF (M-JPDAF). Second, the coalescence-avoiding JPDAF (C-JPDAF) is derived, which removes coalescence by minimizing a weighted sum of the posterior uncertainty and a measure of similarity between estimated probability densities. Both novel algorithms are tested in much further depth than any prior work to show how the algorithms perform in various scenarios. In particular, the M-JPDAF more accurately tracks objects than the conventional JPDAF in all simulated cases. When coalescence degrades the estimates at too great of a level, and the C-JPDAF is often superior at removing coalescence when its parameters are properly tuned.

Keywords

Data association JPDAF Minimum uncertainty Coalescence 

References

  1. 1.
    Bar-Shalom, Y.: Multitarget-Multisensor Tracking. Artech House, Inc., Norwood (1990)MATHGoogle Scholar
  2. 2.
    Bar-Shalom, Y., Daum, F., Huang, J.: The probabilistic data association filter. IEEE Control. Syst. Mag. 29, 82–100 (2009)MathSciNetCrossRefGoogle Scholar
  3. 3.
    Bar-Shalom, Y., Fortmann, T.E.: Tracking and Data Association. Elsevier, Inc, San Diego (1988)MATHGoogle Scholar
  4. 4.
    Bar-Shalom, Y., Li, X.R., Kirubarajan, T.: Estimation with Applications to Tracking and Navigation. A Wiley-Interscience Publication, New York (2001)CrossRefGoogle Scholar
  5. 5.
    Blackman, S.S.: Multiple hypothesis tracking for multiple target tracking. IEEE Aerosp. Electron. Syst. Mag. 19(1), 5–18 (2004)CrossRefGoogle Scholar
  6. 6.
    Bloem, E.A., Blom, H.A.: Joint probabilistic data association methods avoiding track coalescence. In: Proceedings of the IEEE Conference on Decision and Control (1995)Google Scholar
  7. 7.
    Blom, H.A., Bloem, E.A.: Combining IMM and JPDA for tracking multiple maneuvering targets in clutter. Tech. rep., National Aerospace Laboratory NLR (2002)Google Scholar
  8. 8.
    Blom, H.A.P., Bloem, E.: Probabilistic data association avoiding track coalescence. IEEE Trans. Autom. Control 45(17), 247–259 (2000)MathSciNetCrossRefMATHGoogle Scholar
  9. 9.
    Chen, S., Xu, Y.: A new joint probabilistic data association algorithm avoiding track coalescence. Int. J. Intell. Syst. Appl. 2, 45–51 (2011)Google Scholar
  10. 10.
    Fitzgerald, R.J.: Development of practical PDA logic for multitarget tracking by microprocessor. Tech. rep., Raytheon Company, Missile Systems Division (1986)Google Scholar
  11. 11.
    Gelb, A.: Applied Optimal Estimation. The Analytic Sciences Corporation, Cambridge (1974)Google Scholar
  12. 12.
    Habtemariam, B., Tharmarasa, R., Thayaparan, T., Mallick, M., Kirubarajan, T.: A multiple-detection joint probabilistic data association filter. IEEE J. Sel. Top. Sign. Process. 7(3), 461–471 (2013)CrossRefGoogle Scholar
  13. 13.
    Kaufman, E., Lovell, T.A., Lee, T.: Optimal joint probabilistic data association filter avoiding coalescence in close proximity. In: Proceedings of the IEEE European Control Conference (2014)Google Scholar
  14. 14.
    Kaufman, E., Lovell, T.A., Lee, T.: Minimum uncertainty jpda filter and coalescence avoidance performance evaluations. In: Proceedings of the AAS-AIAA Spaceflight Mechanics Meeting (2015)Google Scholar
  15. 15.
    Kural, F., Arikan, F., Arikan, O., Efe, M.: Performance evaluation of track association and maintenance for a mfpar with doppler velocity measurements. Prog. Electromagn. Res. 108, 249–275 (2010)CrossRefGoogle Scholar
  16. 16.
    Li, X.R., Bar-Shalom, Y.: Tracking in clutter with nearest neighbor filters: analysis and performance. IEEE Trans. Aerosp. Electron. Syst. 32(3), 995–1010 (1996)CrossRefGoogle Scholar
  17. 17.
    Lin, L., Bar-Shalom, Y., Kirubarajan, T.: Track labeling and PHD filter for multitarget tracking. IEEE Trans. Aerosp. Electron. Syst. 42(3), 778–794 (2006)CrossRefGoogle Scholar
  18. 18.
    Matusita, K.: Decision rules, based on the distance, for problems of fit, two samples, and estimation. Ann. Math. Stat. 26(4), 631–640 (1955)MathSciNetCrossRefMATHGoogle Scholar
  19. 19.
    Minami, M., Shimizu, K.: Estimation of similarity measure for multivariate normal distributions. Environ. Ecol. Stat. 45(6), 229–248 (1999)CrossRefGoogle Scholar
  20. 20.
    Sidenbladh, H.: Development of practical PDA logic for multitarget tracking by microprocessor. In: Proceedings of the American Control Conference (1986)Google Scholar
  21. 21.
    Svensson, D., Ulmke, M., Danielson, L.: Joint probabilistic data association filter for partially unresolved target groups. In: 13th Conference on Information Fusion (FUSION) (2010)Google Scholar
  22. 22.
    Thacker, N.A., Aherne, F.J., Rockett, P.I.: The bhattacharyya metric as an absolute similarity measure for frequency coded data. Kybernetika 34(4), 363–368 (1997)MathSciNetMATHGoogle Scholar
  23. 23.
    Vallado, D.A.: Fundamentals of Astrodynamics and Applications. Microcosm Press and Kluwer Academic Publishers, El Segundo (2001)MATHGoogle Scholar
  24. 24.
    Vergez, P., Sauter, L., Dahlke, S.: An improved kalman filter for satellite orbit predictions. J. Astronaut. Sci. 52, 7 (2004)MathSciNetGoogle Scholar
  25. 25.
    Wei, X., Jing-Wei, Z., You, H.: Multisensor multitarget tracking methods based on particle filter. In: Proceedings of Autonomous Decentralized Systems (2010)Google Scholar

Copyright information

© American Astronautical Society 2016

Authors and Affiliations

  1. 1.Department of Mechanical and Aerospace EngineeringThe George Washington UniversityWashingtonUSA
  2. 2.Air Force Research LaboratorySpace Vehicles DirectorateKirtland AFBUSA

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