Convex optimization approach to identity fusion for multisensor target tracking

Ngjie Li, Zhi Quan Luo, K. Max Wong, Eloi Bossé

Research output: Contribution to journalArticlepeer-review

15 Scopus citations


We consider the problem of identity fusion for a multisensor target tracking system whereby sensors generate reports on the target identities. Since sensor reports are typically fuzzy, incomplete, or inconsistent, the fusion of such sensor reports becomes a major challenge. In this paper, we introduce a new identity fusion method based on the minimization of inconsistencies among the sensor reports by using a convex quadratic programming (QP) formulation. In contrast to Dempster-Shafer's (D-S) evidential reasoning approach which suffers from exponentially growing complexity, our approach is highly efficient (polynomial time solvable). Moreover, our approach can fuse sensor reports of the form more general than that allowed by the evidential reasoning theory. Simulation results show that our method generates reasonable fusion results which are similar to that obtained via the evidential reasoning theory.

Original languageEnglish (US)
Pages (from-to)172-178
Number of pages7
JournalIEEE Transactions on Systems, Man, and Cybernetics Part A:Systems and Humans.
Issue number3
StatePublished - May 2001

Bibliographical note

Funding Information:
Manuscript received July 1, 1999; revised January 25, 2001. This work was supported by a grant from the Defense Research Establishment of Canada at Valcartier. This paper was recommended by Associate Editor R. Popp.


  • Convex quadratic optimization
  • Decision fusion
  • Dempster-Shafer (D-S) evidential reasoning
  • Identity fusion
  • Multitarget tracking


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