Target Filtering and Tracking

Part of the Signals and Communication Technology book series (SCT)


Up to this point, we have presented methods for detection of target properties, specifically, range, velocity, and DOA. Although this information representing instantaneous target state could be the main objective of radar processing, in automotive radar processing, tracking moving targets is of paramount importance. The processing of detected radar targets using filtering and tracking methods for the purpose of capturing target motion dynamics is the goal of this chapter.


  1. 1.
    Kalman, R.E.: A new approach to linear filtering and prediction problems. J. Basic Eng. 82(Series D), 35–45 (1960)CrossRefGoogle Scholar
  2. 2.
    Gordon, N.J., Salmond, D.J., Smith, A.F.M.: Novel approach to nonlinear/non-Gaussian Bayesian state estimation. In: Radar and Signal Processing, IEE Proceedings F, vol. 140, issue 2, pp. 107–113, Apr 1993CrossRefGoogle Scholar
  3. 3.
    Bar‐Shalom, Y., Li, X., Kirubarajan, T.: Estimation with Applications to Tracking and Navigation: Theory, Algorithms and Software. Wiley (2002)Google Scholar
  4. 4.
    Doucet, A., de Freitas, N., Gordon, N. (eds.): Sequential Monte Carlo Methods in Practice (2001)Google Scholar
  5. 5.
    Welch, G., Bishop, G.: An Introduction to the Kalman Filter. University of North Carolina at Chapel Hill, Chapel Hill, NC (2006)Google Scholar
  6. 6.
    Haykin, S.: Adaptive Filter Theory, 3rd edn. Prentice-Hall, Inc., Upper Saddle River, NJ, USA (1996)zbMATHGoogle Scholar
  7. 7.
    Julier, S.J., Uhlmann, J.K.: Unscented filtering and nonlinear estimation. Proc. IEEE 92(3), 401–422 (2004)CrossRefGoogle Scholar
  8. 8.
    Julier, S.J., Uhlmann, J.K., Durrant-Whyte, H.: A new approach for filtering nonlinear systems. In: Proceedings of the American Control Conference, pp. 1628–1632 (1995)Google Scholar
  9. 9.
    Julier, S.J., Uhlmann, J.K.: A general method for approximating nonlinear transformations of probability distributions. Technical Report, RRG, Department of Engineering Science, University of Oxford, Nov 1996Google Scholar
  10. 10.
    Julier, S.J., Uhlmann, J.K.: A new extension of the Kalman filter to nonlinear systems. In: Proceedings of AeroSense: The 11th International Symposium on Aerospace/Defence Sensing, Simulation and Controls (1997)Google Scholar
  11. 11.
    Julier, S.J., Uhlmann, J.K., Durrant-Whyte, H.: A new method for the nonlinear transformation of means and covariances in filters and estimators. IEEE Trans. Autom. Control 45(3), 477–482 (2000)MathSciNetCrossRefGoogle Scholar
  12. 12.
    Wan, E.A., van der Merwe, R.: The Unscented Kalman Filter. In: Haykin, S. (ed.) Kalman Filtering and Neural Networks. Wiley (2001)Google Scholar
  13. 13.
    LaViola, J.J.: A comparison of unscented and extended Kalman filtering for estimating quaternion motion. In: Proceedings on American Control Conference, vol. 3, pp. 2435–2440 (2003)Google Scholar
  14. 14.
    Andrieu, C., Doucet, A., Holenstein, R.: Particle Markov chain Monte Carlo methods. J. R. Stat. Soc. Ser. B 72(Part 3), 269–342 (2010)MathSciNetCrossRefGoogle Scholar
  15. 15.
    Doucet, A., Johansen, A.M.: A tutorial on particle filtering and smoothing: fifteen years later. In: Crisan, D., Rozovsky, B. (eds.) Handbook of Nonlinear Filtering. Cambridge University Press, Cambridge (2009)Google Scholar
  16. 16.
    Gustafsson, Fredrik: Particle filter theory and practice with positioning applications. IEEE Aerosp. Electron. Syst. Mag. 25(7), 53–82 (2010)CrossRefGoogle Scholar
  17. 17.
    van der Merwe, R., Doucet, A., de Freitas, J.F.G., Wan, E.: The unscented particle filter. Technical Report CUED/F-INFENG/TR 380, Cambridge University Engineering Department (2000)Google Scholar
  18. 18.
    Gamba, J., Yamano, C.: Target tracking using parallel partition particle filtering. In: Society Conference of IEICE, Hokkaido University (Sapporo), Japan, 20–23 Sept 2005Google Scholar
  19. 19.
    Hol, J.D., Schon, T.B., Gustafsson, F.: On resampling algorithms for particle filters. In: 2006 IEEE Nonlinear Statistical Signal Processing Workshop, 13–15 Sept 2006Google Scholar
  20. 20.
    Li, T., Bolic, M., Djuric, P.M.: Resampling methods for particle filtering: classification, implementation, and strategies. IEEE Signal Process. Mag. 32(3), 70–86 (2015)CrossRefGoogle Scholar
  21. 21.
    Särkkä, S., Vehtari, A., Lampinen, J.: Rao-Blackwellized particle filter for multiple target tracking. Inf. Fusion 8(1), 2–15 (2007)CrossRefGoogle Scholar
  22. 22.
    Kirubarajan, T., Bar-Shalom, Y.: Probabilistic data association techniques for target tracking in clutter. Proc. IEEE 92(3), 536–557 (2004)CrossRefGoogle Scholar
  23. 23.
    Kuhn, H.W.: The Hungarian method for the assignment problem. Naval Res. Logistics Q. 2(1–2), 83–97 (1955)MathSciNetCrossRefGoogle Scholar
  24. 24.
    Munkres, J.: Algorithms for assignment and transportation problems. J. Soc. Ind. Appl. Math. 5(1), 32–38 (1957)MathSciNetCrossRefGoogle Scholar
  25. 25.
    Murty, K.G.: An algorithm for ranking all the assignments in order of increasing cost. Oper. Res. 16(3), 682–687 (1968)CrossRefGoogle Scholar
  26. 26.
    Jonker, R., Volgenant, T.: A shortest augmenting path algorithm for dense and sparse linear assignment problems. Computing 38(11), 325–340 (1987)MathSciNetCrossRefGoogle Scholar
  27. 27.
    Poore, A.B., Gadaleta, S.: Some assignment problems arising from multiple target tracking. Math. Comput. Model. 43, 1074–1091 (2006)MathSciNetCrossRefGoogle Scholar
  28. 28.
  29. 29.
    Heuer, M., Al-Hamadi, A., Rain, A., Meinecke, M., Rohling, H.: Pedestrian tracking with occlusion using a 24 GHz automotive radar. In: 2014 15th International Radar Symposium (IRS), 16–18 June 2014Google Scholar
  30. 30.
    Gaddigoudar, P.K., Balihalli, T.R., Ijantkar, S.S., Iyer, N.C., Maralappanavar, S.: Pedestrian detection and tracking using particle filtering. In: 2017 International Conference on Computing, Communication and Automation (ICCCA), pp. 110–115 (2017)Google Scholar

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© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  1. 1.TsukubaJapan

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