Skip to main content
SpringerLink
Log in

On the Quality of Optimal Assignment for Data Association

  • Conference paper
Book cover Belief Functions: Theory and Applications (BELIEF 2014)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 8764))

Included in the following conference series:

Abstract

In this paper, we present a method based on belief functions to evaluate the quality of the optimal assignment solution of a classical association problem encountered in multiple target tracking applications. The purpose of this work is not to provide a new algorithm for solving the assignment problem, but a solution to estimate the quality of the individual associations (pairings) given in the optimal assignment solution. To the knowledge of authors, this problem has not been addressed so far in the literature and its solution may have practical aspects for improving the performances of multisensor-multitarget tracking systems.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Bar-Shalom, Y., Willet, P.K., Tian, X.: Tracking and Data Fusion: A Handbook of Algorithms. YBS Publishing, Storrs (2011)

    Google Scholar 

  2. Hall, D.L., Chong, C.Y., Llinas, J., Liggins II, M.: Distributed Data Fusion for Network-Centric Operations. CRC Press (2013)

    Google Scholar 

  3. He, X., Tharmarasa, R., Pelletier, M., Kirubarajan, T.: Accurate Murty’s Algorithm for Multitarget Top Hypothesis. In: Proc. of Fusion 2011, Chicago, USA (2011)

    Google Scholar 

  4. Dezert, J., Smarandache, F., Tchamova, A.: On the Blackman’s Association Problem. In: Proc. of Fusion 2003, Cairns, Australia (2003)

    Google Scholar 

  5. Tchamova, A., Dezert, J., Semerdjiev, T., Konstantinova, P.: Target Tracking with Generalized Data Association Based on the General DSm Rule of Combination. In: Proc. of Fusion 2004, Stockholm, Sweden (2004)

    Google Scholar 

  6. Dezert, J., Tchamova, A., Semerdjiev, T., Konstantinova, P.: Performance Evaluation of Fusion Rules For Multitarget Tracking in Clutter Based on Generalized Data Association. In: Proc. of Fusion 2005, Philadelphia, USA (2005)

    Google Scholar 

  7. Denœux, T., El Zoghby, N., Cherfaoui, V., Jouglet, A.: Optimal Object Association in the Dempster- Shafer Framework. To appear in IEEE Trans. on Cybern (2014)

    Google Scholar 

  8. Kuhn, H.W.: The Hungarian Method for the Assignment Problem. Naval Research Logistic Quarterly 2, 83–97 (1955)

    Article  MathSciNet  MATH  Google Scholar 

  9. Munkres, J.: Algorithms for the Assignment and Transportation Problems. Journal of the Society of Industrial and Applied Mathematics 5(1), 32–38 (1957)

    Article  MathSciNet  MATH  Google Scholar 

  10. Bourgeois, F., Lassalle, J.C.: An Extension of the Munkres Algorithm for the Assignment Problem to Rectangular Matrices. Comm. of the ACM 14(12), 802–804 (1971)

    Article  MathSciNet  MATH  Google Scholar 

  11. Jonker, R., Volgenant, A.: A shortest augmenting path algorithm for dense and sparse linear assignment problems. J. of Comp. 38(4), 325–340 (1987)

    MathSciNet  MATH  Google Scholar 

  12. Bertsekas, D.: The auction algorithm: A Distributed Relaxation Method for the Assignment Problem. Annals of Operations Research 14(1), 105–123 (1988)

    Article  MathSciNet  MATH  Google Scholar 

  13. Murty, K.G.: An Algorithm for Ranking all the Assignments in Order of Increasing Cost. Operations Research 16(3), 682–687 (1968)

    Article  MATH  Google Scholar 

  14. Chegireddy, C.R., Hamacher, H.W.: Algorithms for Finding K-best Perfect Matching. Discrete Applied Mathematics 18, 155–165 (1987)

    Article  MathSciNet  MATH  Google Scholar 

  15. Danchick, R., Newnam, G.E.: A Fast Method for Finding the Exact N-best Hypotheses for Multitarget Tracking. IEEE Trans. on AES 29(2), 555–560 (1993)

    Google Scholar 

  16. Miller, M.L., Stone, H.S., Cox, I.J.: Optimizing Murty’s Ranked Assignment Method. IEEE Trans. on AES 33(3), 851–862 (1997)

    Google Scholar 

  17. Pascoal, M., Captivo, M.E., Climaco, J.: A Note on a New Variant of Murty’s Ranking Assignments Algorithm. 4OR Journal 1, 243–255 (2003)

    MathSciNet  MATH  Google Scholar 

  18. Ding, Z., Vandervies, D.: A Modified Murty’s Algorithm for Multiple Hypothesis Tracking. In: SPIE Sign. and Data Proc. of Small Targets, vol. 6236, (2006)

    Google Scholar 

  19. Fortunato, E., et al.: Generalized Murty’s Algorithm with Application to Multiple Hypothesis Tracking. In: Proc. of Fusion 2007, Québec, July 9-12, pp. 1–8 (2007)

    Google Scholar 

  20. Hamacher, H.W., Queyranne, M.: K-best Solutions to Combinatorial Optimization Problems. Annals of Operations Research (4), 123–143 (1985)

    Google Scholar 

  21. Dell’Amico, M., Toth, P.: Algorithms and Codes for Dense Assignment Problems: the State of the Art. Discrete Applied Mathematics 100, 17–48 (2000)

    Article  MathSciNet  MATH  Google Scholar 

  22. Shafer, G.: A Mathematical Theory of Evidence. Princeton University Press, Princeton (1976)

    Google Scholar 

  23. Smarandache, F., Dezert, J.: Advances and Applications of DSmT for Information Fusion, vols. 1, 2 & 3. ARP (2004–2009), http://www.gallup.unm.edu/~smarandache/DSmT.htm

  24. Smets, P., Kennes, R.: The Transferable Belief Model. Artificial Intelligence 66(2), 191–234 (1994)

    Article  MathSciNet  MATH  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. The French Aerospace Lab, Chemin de la Hunière, F-91761, Palaiseau, France

    Jean Dezert & Kaouthar Benameur

Authors
  1. Jean Dezert
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Kaouthar Benameur
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Department of Computing and Communications Technologies, Oxford Brookes University, Wheatley Campus, OX33 1HX, Oxford, UK

    Fabio Cuzzolin

Rights and permissions

Reprints and permissions

Copyright information

© 2014 Springer International Publishing Switzerland

About this paper

Cite this paper

Dezert, J., Benameur, K. (2014). On the Quality of Optimal Assignment for Data Association. In: Cuzzolin, F. (eds) Belief Functions: Theory and Applications. BELIEF 2014. Lecture Notes in Computer Science(), vol 8764. Springer, Cham. https://doi.org/10.1007/978-3-319-11191-9_41

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-11191-9_41

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-11190-2

  • Online ISBN: 978-3-319-11191-9

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation