Factor graph aided multiple hypothesis tracking

Huan Wang, Jin Ping Sun, Song Tao Lu, Shao Ming Wei

Research output: Contribution to journalArticlepeer-review

17 Scopus citations

Abstract

Since closely moving targets exist extensively in the ground moving target tracking, the uncertainty of data association greatly increases making the measurement-to-track association more difficult. Especially, traditional multiple hypothesis tracking (MHT) has high false tracking rate and track swap. This paper first investigates the measurement based factor graph in data association, and gives the corresponding message passing algorithm. Then, a factor graph aided multiple hypothesis tracking (FGA-MHT) method is proposed, which introduces factor graph based m-best hypothesis producing technique and exploits factor graph based probability refinement algorithm to reduce the uncertainty of measurement-to-track association. Experiment results demonstrate that FGA-MHT reduces times of track swap and increases the correct data association rate in closely moving target tracking.

Original languageEnglish (US)
Pages (from-to)1-6
Number of pages6
JournalScience China Information Sciences
Volume56
Issue number10
DOIs
StatePublished - Oct 2013

Bibliographical note

Funding Information:
This work was supported by National Basic Research Program of China (973) (Grant No.

Keywords

  • data association
  • factor graph
  • factor graph aided multiple hypothesis tracking (FGA-MHT)
  • message passing algorithm
  • multiple hypothesis tracking (MHT)
  • sum-product algorithm

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