Multi-sensor targets data association and track fusion based on novel AWFCM

Xiang Wang, Rui Guo, Niraj K. Jha, Wayne Wolf, Qishao Lu

Research output: Contribution to journalArticle

6 Scopus citations

Abstract

In view of the complex character of traditional JPDA algorithms to realize the data association, a novel AWFCM algorithm aiming at realizing the multi-target tracking under the cross tracks situation was proposed in this article. This new algorithm defined a new distance in new metric space in order to efficiently restrain the error range of data association clustering centers for samples with noise points and cross tracks. Also the improved weights based on the dots' density were introduced to the algorithm to reduce the negative influence of the imbalanced data sets. In this way, the improved weighted track fusion algorithm realized IR and MMW radar sensors' track fusion. Simulations have proved the validity of the AWFCM algorithm considering the advantages of the multi-sensor and the process complexity. The new system provides a reliable and valid method to make multi-target tracking data association and track fusion.

Original languageEnglish (US)
Pages (from-to)811-821
Number of pages11
JournalInternational Journal of Nonlinear Sciences and Numerical Simulation
Volume10
Issue number6
DOIs
StatePublished - Jun 2009

All Science Journal Classification (ASJC) codes

  • Statistical and Nonlinear Physics
  • Computational Mechanics
  • Modeling and Simulation
  • Engineering (miscellaneous)
  • Mechanics of Materials
  • Physics and Astronomy(all)
  • Applied Mathematics

Keywords

  • AWFCM
  • Distance
  • Multi-target tracking
  • Track fusion
  • Weighted

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